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clustering analysis</title> <meta name="description" content="Search results for: space-time clustering analysis"> <meta name="keywords" content="space-time clustering analysis"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="space-time clustering analysis"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 28197</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: space-time clustering analysis</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28197</span> Creation and Annihilation of Spacetime Elements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dnyanesh%20P.%20Mathur">Dnyanesh P. Mathur</a>, <a href="https://publications.waset.org/abstracts/search?q=Gregory%20L.%20Slater"> Gregory L. Slater</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gravitation and the expansion of the universe at a large scale are generally regarded as two completely distinct phenomena. Yet, in general, relativity theory, they both manifest as 'curvature' of spacetime. We propose a hypothesis which treats these two 'curvature-producing' phenomena as aspects of an underlying process. This process treats spacetime itself as composed of discrete units (Plancktons) and is 'dynamic' in the sense that these elements of spacetime are continually being both created and annihilated. It is these two complementary processes of Planckton creation and Planckton annihilation which manifest themselves as - 'cosmic expansion' on the one hand and as 'gravitational attraction’ on the other. The Planckton hypothesis treats spacetime as a perfect fluid in the same manner as the co-moving frame of reference of Friedman equations and the Gullstrand-Painleve metric; i.e.Planckton hypothesis replaces 'curvature' of spacetime by the 'flow' of Plancktons (spacetime). Here we discuss how this perspective may allow a unified description of both cosmological and gravitational acceleration as well as providing a mechanism for inducing an irreducible action at every point associated with the creation and annihilation of Plancktons, which could be identified as the zero point energy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20spacetime" title="discrete spacetime">discrete spacetime</a>, <a href="https://publications.waset.org/abstracts/search?q=spacetime%20flow" title=" spacetime flow"> spacetime flow</a>, <a href="https://publications.waset.org/abstracts/search?q=zero%20point%20energy" title=" zero point energy"> zero point energy</a>, <a href="https://publications.waset.org/abstracts/search?q=planktons" title=" planktons"> planktons</a> </p> <a href="https://publications.waset.org/abstracts/156001/creation-and-annihilation-of-spacetime-elements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156001.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">114</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28196</span> The Grand Unified Theory of Bidirectional Spacetime with Spatial Covariance and Wave-Particle Duality in Spacetime Flow Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tory%20Erickson">Tory Erickson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The "Bidirectional Spacetime with Spatial Covariance and Wave-Particle Duality in Spacetime Flow" (BST-SCWPDF) Model introduces a framework aimed at unifying general relativity (GR) and quantum mechanics (QM). By proposing a concept of bidirectional spacetime, this model suggests that time can flow in more than one direction, thus offering a perspective on temporal dynamics. Integrated with spatial covariance and wave-particle duality in spacetime flow, the BST-SCWPDF Model resolves long-standing discrepancies between GR and QM. This unified theory has profound implications for quantum gravity, potentially offering insights into quantum entanglement, the collapse of the wave function, and the fabric of spacetime itself. The Bidirectional Spacetime with Spatial Covariance and Wave-Particle Duality in Spacetime Flow" (BST-SCWPDF) Model offers researchers a framework for a better understanding of theoretical physics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=astrophysics" title="astrophysics">astrophysics</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20mechanics" title=" quantum mechanics"> quantum mechanics</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20relativity" title=" general relativity"> general relativity</a>, <a href="https://publications.waset.org/abstracts/search?q=unification%20theory" title=" unification theory"> unification theory</a>, <a href="https://publications.waset.org/abstracts/search?q=theoretical%20physics" title=" theoretical physics"> theoretical physics</a> </p> <a href="https://publications.waset.org/abstracts/183765/the-grand-unified-theory-of-bidirectional-spacetime-with-spatial-covariance-and-wave-particle-duality-in-spacetime-flow-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183765.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28195</span> Non-Singular Gravitational Collapse of a Dust Cloud in Einstein-Cartan Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Hadi%20Ziaie">Amir Hadi Ziaie</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Hashemi"> Mostafa Hashemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahram%20Jalalzadeh"> Shahram Jalalzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is now known that the end state of the collapse process of a dense star under its own gravity is the formation of a spacetime singularity. This is the spacetime event where the energy density and spacetime curvature diverge, and the classical general relativity breaks down. As we know, a realistic star is composed of fermions so that their spin effects could alter the final fate of the collapse scenario. The underlying theory within which the inclusion of spin effects can be worked out is the Einstein-Cartan theory. In this theory, the spacetime torsion which is defined as a geometrical quantity, is related to an intrinsic angular momentum of fermions (spin). In this work, we study the collapse process of a homogeneous spin fluid in such a framework and show that taking into account the spin effects of the collapsing cloud could prevent the formation of spacetime singularity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gravitational%20collapse" title="gravitational collapse">gravitational collapse</a>, <a href="https://publications.waset.org/abstracts/search?q=einstein-cartan%20theory" title=" einstein-cartan theory"> einstein-cartan theory</a>, <a href="https://publications.waset.org/abstracts/search?q=spacetime%20singularity" title=" spacetime singularity"> spacetime singularity</a>, <a href="https://publications.waset.org/abstracts/search?q=black%20hole%20physics" title=" black hole physics"> black hole physics</a> </p> <a href="https://publications.waset.org/abstracts/50866/non-singular-gravitational-collapse-of-a-dust-cloud-in-einstein-cartan-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50866.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">398</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28194</span> Space Tourism Pricing Model Revolution from Time Independent Model to Time-Space Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kang%20Lin%20Peng">Kang Lin Peng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Space tourism emerged in 2001 and became famous in 2021, following the development of space technology. The space market is twisted because of the excess demand. Space tourism is currently rare and extremely expensive, with biased luxury product pricing, which is the seller’s market that consumers can not bargain with. Spaceship companies such as Virgin Galactic, Blue Origin, and Space X have been charged space tourism prices from 200 thousand to 55 million depending on various heights in space. There should be a reasonable price based on a fair basis. This study aims to derive a spacetime pricing model, which is different from the general pricing model on the earth’s surface. We apply general relativity theory to deduct the mathematical formula for the space tourism pricing model, which covers the traditional time-independent model. In the future, the price of space travel will be different from current flight travel when space travel is measured in lightyear units. The pricing of general commodities mainly considers the general equilibrium of supply and demand. The pricing model considers risks and returns with the dependent time variable as acceptable when commodities are on the earth’s surface, called flat spacetime. Current economic theories based on the independent time scale in the flat spacetime do not consider the curvature of spacetime. Current flight services flying the height of 6, 12, and 19 kilometers are charging with a pricing model that measures time coordinate independently. However, the emergence of space tourism is flying heights above 100 to 550 kilometers that have enlarged the spacetime curvature, which means tourists will escape from a zero curvature on the earth’s surface to the large curvature of space. Different spacetime spans should be considered in the pricing model of space travel to echo general relativity theory. Intuitively, this spacetime commodity needs to consider changing the spacetime curvature from the earth to space. We can assume the value of each spacetime curvature unit corresponding to the gradient change of each Ricci or energy-momentum tensor. Then we know how much to spend by integrating the spacetime from the earth to space. The concept is adding a price p component corresponding to the general relativity theory. The space travel pricing model degenerates into a time-independent model, which becomes a model of traditional commodity pricing. The contribution is that the deriving of the space tourism pricing model will be a breakthrough in philosophical and practical issues for space travel. The results of the space tourism pricing model extend the traditional time-independent flat spacetime mode. The pricing model embedded spacetime as the general relativity theory can better reflect the rationality and accuracy of space travel on the universal scale. The universal scale from independent-time scale to spacetime scale will bring a brand-new pricing concept for space traveling commodities. Fair and efficient spacetime economics will also bring to humans’ travel when we can travel in lightyear units in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=space%20tourism" title="space tourism">space tourism</a>, <a href="https://publications.waset.org/abstracts/search?q=spacetime%20pricing%20model" title=" spacetime pricing model"> spacetime pricing model</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20relativity%20theory" title=" general relativity theory"> general relativity theory</a>, <a href="https://publications.waset.org/abstracts/search?q=spacetime%20curvature" title=" spacetime curvature"> spacetime curvature</a> </p> <a href="https://publications.waset.org/abstracts/141651/space-tourism-pricing-model-revolution-from-time-independent-model-to-time-space-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141651.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28193</span> Effect of the Tidal Charge Parameter on CMBR Temperature Anisotropies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Evariste%20Boj">Evariste Boj</a>, <a href="https://publications.waset.org/abstracts/search?q=Jan%20Schee"> Jan Schee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present the temperature anisotropy of the cosmic microwave background radiation due to the inhomogeneity region constructed on a 3-brane in the framework of a Randall-Sundrum one brane immersed into a 5D bulk $AdS_5$ spacetime. We employ the Brane-World Friedmann-Lemaitre-Robertson-Walker (FLRW) cosmological model to describe the cosmic expansion on the brane. The inhomogeneity is modeled by the static, spherically symmetric spacetime that replaces the spherically symmetric part of a dust-filled universe and is connected to the FLRW spacetime through the junction conditions. As the vacuum region expands it induces an additional frequency shift to a CMBR photon passing through this inhomogeneity in comparison to the case of a photon propagating through a pure FLRW spacetime. This frequency shift is associated with the effective temperature change of the CMBR in the corresponding direction. We give an estimate of the CMBR effective temperature changes with the change of the value of the tidal charge parameter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CMBR" title="CMBR">CMBR</a>, <a href="https://publications.waset.org/abstracts/search?q=Randall-Sundrum%20model" title=" Randall-Sundrum model"> Randall-Sundrum model</a>, <a href="https://publications.waset.org/abstracts/search?q=Rees-Sciama%20effect" title=" Rees-Sciama effect"> Rees-Sciama effect</a>, <a href="https://publications.waset.org/abstracts/search?q=Braneworld" title=" Braneworld"> Braneworld</a> </p> <a href="https://publications.waset.org/abstracts/140418/effect-of-the-tidal-charge-parameter-on-cmbr-temperature-anisotropies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140418.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">214</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28192</span> Fuzzy Optimization Multi-Objective Clustering Ensemble Model for Multi-Source Data Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20B.%20Le">C. B. Le</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20N.%20Pham"> V. N. Pham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering%20ensemble" title="clustering ensemble">clustering ensemble</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-source" title=" multi-source"> multi-source</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective" title=" multi-objective"> multi-objective</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20clustering" title=" fuzzy clustering"> fuzzy clustering</a> </p> <a href="https://publications.waset.org/abstracts/136598/fuzzy-optimization-multi-objective-clustering-ensemble-model-for-multi-source-data-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136598.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">189</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28191</span> Hybrid Hierarchical Clustering Approach for Community Detection in Social Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Radhia%20Toujani">Radhia Toujani</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalel%20Akaichi"> Jalel Akaichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agglomerative%20hierarchical%20clustering" title="agglomerative hierarchical clustering">agglomerative hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=community%20structure" title=" community structure"> community structure</a>, <a href="https://publications.waset.org/abstracts/search?q=divisive%20hierarchical%20clustering" title=" divisive hierarchical clustering"> divisive hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20hierarchical%20clustering" title=" hybrid hierarchical clustering"> hybrid hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion%20mining" title=" opinion mining"> opinion mining</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network" title=" social network"> social network</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20analysis" title=" social network analysis"> social network analysis</a> </p> <a href="https://publications.waset.org/abstracts/63702/hybrid-hierarchical-clustering-approach-for-community-detection-in-social-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63702.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">365</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28190</span> Performance Analysis of Hierarchical Agglomerative Clustering in a Wireless Sensor Network Using Quantitative Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tapan%20Jain">Tapan Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Davender%20Singh%20Saini"> Davender Singh Saini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=routing" title="routing">routing</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20clustering" title=" hierarchical clustering"> hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=agglomerative" title=" agglomerative"> agglomerative</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative" title=" quantitative"> quantitative</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20network" title=" wireless sensor network"> wireless sensor network</a> </p> <a href="https://publications.waset.org/abstracts/3593/performance-analysis-of-hierarchical-agglomerative-clustering-in-a-wireless-sensor-network-using-quantitative-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">615</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28189</span> Modifying Hawking Radiation in 2D-Approximated Schwarzschild Black Holes near the Event Horizon</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Richard%20Pincak">Richard Pincak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Starting from a 4D spacetime model using a partially negative dimensional product manifold (PNDP-manifold), which emerges as a 2D spacetime, we developed an analysis of tidal forces and Hawking radiation near the event horizon of a Schwarzchild black hole. The modified 2D metric, incorporating the effects of the four-dimensional Weyl tensor, with the dilatonic field and the newly derived time relation $2\alpha t = \ln \epsilon$, can enable a deeper understanding of quantum gravity. The analysis shows how the modified Hawking temperature and distribution of emitted particles are affected by additional fields, providing potential observables for future experiments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=black%20holes" title="black holes">black holes</a>, <a href="https://publications.waset.org/abstracts/search?q=Hawking%20radiation" title=" Hawking radiation"> Hawking radiation</a>, <a href="https://publications.waset.org/abstracts/search?q=Weyl%20tensor" title=" Weyl tensor"> Weyl tensor</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20paradox" title=" information paradox"> information paradox</a> </p> <a href="https://publications.waset.org/abstracts/191161/modifying-hawking-radiation-in-2d-approximated-schwarzschild-black-holes-near-the-event-horizon" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191161.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">21</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28188</span> Mitigating the Negative Effect of Intrabrand Clustering: The Role of Interbrand Clustering and Firm Size</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moeen%20Naseer%20Butt">Moeen Naseer Butt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering –geographic concentrations of entities– has recently received more attention in marketing research and has been shown to affect multiple outcomes. This study investigates the impact of intrabrand clustering (clustering of same-brand outlets) on an outlet’s quality performance. Further, it assesses the moderating effects of interbrand clustering (clustering of other-brand outlets) and firm size. An examination of approximately 21,000 food service establishments in New York State in 2019 finds that the impact of intrabrand clustering on an outlet’s quality performance is context-dependent. Specifically, intrabrand clustering decreases, whereas interbrand clustering and firm size help increase the outlet’s performance. Additionally, this study finds that the role of firm size is more substantial than interbrand clustering in mitigating the adverse effects of intrabrand clustering on outlet quality performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intraband%20clustering" title="intraband clustering">intraband clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=interbrand%20clustering" title=" interbrand clustering"> interbrand clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=firm%20size" title=" firm size"> firm size</a>, <a href="https://publications.waset.org/abstracts/search?q=brand%20competition" title=" brand competition"> brand competition</a>, <a href="https://publications.waset.org/abstracts/search?q=outlet%20performance" title=" outlet performance"> outlet performance</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20violations" title=" quality violations"> quality violations</a> </p> <a href="https://publications.waset.org/abstracts/155035/mitigating-the-negative-effect-of-intrabrand-clustering-the-role-of-interbrand-clustering-and-firm-size" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155035.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28187</span> Hierarchical Clustering Algorithms in Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Abdullah">Z. Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Hamdan"> A. R. Hamdan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the areas in data mining and it can be classified into partition, hierarchical, density based, and grid-based. Therefore, in this paper, we do a survey and review for four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON, and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems, as well as deriving more robust and scalable algorithms for clustering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithms" title=" algorithms"> algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical" title=" hierarchical"> hierarchical</a> </p> <a href="https://publications.waset.org/abstracts/31217/hierarchical-clustering-algorithms-in-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31217.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">885</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28186</span> A Review on the Problems of Constructing a Theory of Quantum Gravity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amber%20Jamal">Amber Jamal</a>, <a href="https://publications.waset.org/abstracts/search?q=Imran%20Siddiqui"> Imran Siddiqui</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Tanveer%20Iqbal"> Syed Tanveer Iqbal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This review is aimed to shed some light on problems constructing a theory of spacetime and geometry in terms of all quantum degrees of freedom called ‘Quantum Gravity’. Such a theory, which is effective at all scales of distances and energies, describes the enigma of the beginning of the Universe, its possible end, and reducing to general relativity at large distances but in a semi-classical approximation. Furthermore, the theory of quantum gravity also describes the Universe as a whole and provides a description of most fundamental questions that have puzzled scientists for decades, such as: what is space, what is time, and what is the fundamental structure of the Universe, is the spacetime discrete, if it is, where does the continuum of spacetime come from at low energies and macroscopic scales and where does it emerge from its fundamentally discrete building blocks? Quantum Field Theory (QFT) is a framework which describes the microscopic properties and dynamics of the basic building blocks of any condensed matter system. In QFT, atoms are quanta of continuous fields. At smaller scales or higher energies, the continuum description of spacetime fails. Therefore, a new description is required in terms of microscopic constituents (atoms or molecules). The objective of this scientific endeavor is to discuss the above-mentioned problems rigorously and to discuss possible way-out of the problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=QFT" title="QFT">QFT</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20degrees%20of%20freedom" title=" quantum degrees of freedom"> quantum degrees of freedom</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20gravity" title=" quantum gravity"> quantum gravity</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-classical%20approximation" title=" semi-classical approximation"> semi-classical approximation</a> </p> <a href="https://publications.waset.org/abstracts/130610/a-review-on-the-problems-of-constructing-a-theory-of-quantum-gravity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130610.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28185</span> An Experimental Study on Some Conventional and Hybrid Models of Fuzzy Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeugert%20Kujtila">Jeugert Kujtila</a>, <a href="https://publications.waset.org/abstracts/search?q=Kristi%20Hoxhalli"> Kristi Hoxhalli</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramazan%20Dalipi"> Ramazan Dalipi</a>, <a href="https://publications.waset.org/abstracts/search?q=Erjon%20Cota"> Erjon Cota</a>, <a href="https://publications.waset.org/abstracts/search?q=Ardit%20Murati"> Ardit Murati</a>, <a href="https://publications.waset.org/abstracts/search?q=Erind%20Bedalli"> Erind Bedalli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is a versatile instrument in the analysis of collections of data providing insights of the underlying structures of the dataset and enhancing the modeling capabilities. The fuzzy approach to the clustering problem increases the flexibility involving the concept of partial memberships (some value in the continuous interval [0, 1]) of the instances in the clusters. Several fuzzy clustering algorithms have been devised like FCM, Gustafson-Kessel, Gath-Geva, kernel-based FCM, PCM etc. Each of these algorithms has its own advantages and drawbacks, so none of these algorithms would be able to perform superiorly in all datasets. In this paper we will experimentally compare FCM, GK, GG algorithm and a hybrid two-stage fuzzy clustering model combining the FCM and Gath-Geva algorithms. Firstly we will theoretically dis-cuss the advantages and drawbacks for each of these algorithms and we will describe the hybrid clustering model exploiting the advantages and diminishing the drawbacks of each algorithm. Secondly we will experimentally compare the accuracy of the hybrid model by applying it on several benchmark and synthetic datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20clustering" title="fuzzy clustering">fuzzy clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c-means%20algorithm%20%28FCM%29" title=" fuzzy c-means algorithm (FCM)"> fuzzy c-means algorithm (FCM)</a>, <a href="https://publications.waset.org/abstracts/search?q=Gustafson-Kessel%20algorithm" title=" Gustafson-Kessel algorithm"> Gustafson-Kessel algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20clustering%20model" title=" hybrid clustering model"> hybrid clustering model</a> </p> <a href="https://publications.waset.org/abstracts/67863/an-experimental-study-on-some-conventional-and-hybrid-models-of-fuzzy-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67863.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">514</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28184</span> Flowing Online Vehicle GPS Data Clustering Using a New Parallel K-Means Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Orhun%20Vural">Orhun Vural</a>, <a href="https://publications.waset.org/abstracts/search?q=Oguz%20%20Bayat"> Oguz Bayat</a>, <a href="https://publications.waset.org/abstracts/search?q=Rustu%20Akay"> Rustu Akay</a>, <a href="https://publications.waset.org/abstracts/search?q=Osman%20N.%20Ucan"> Osman N. Ucan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents a new parallel approach clustering of GPS data. Evaluation has been made by comparing execution time of various clustering algorithms on GPS data. This paper aims to propose a parallel based on neighborhood K-means algorithm to make it faster. The proposed parallelization approach assumes that each GPS data represents a vehicle and to communicate between vehicles close to each other after vehicles are clustered. This parallelization approach has been examined on different sized continuously changing GPS data and compared with serial K-means algorithm and other serial clustering algorithms. The results demonstrated that proposed parallel K-means algorithm has been shown to work much faster than other clustering algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parallel%20k-means%20algorithm" title="parallel k-means algorithm">parallel k-means algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20clustering" title=" parallel clustering"> parallel clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20on%20flowing%20data" title=" clustering on flowing data"> clustering on flowing data</a> </p> <a href="https://publications.waset.org/abstracts/86622/flowing-online-vehicle-gps-data-clustering-using-a-new-parallel-k-means-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86622.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">222</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28183</span> Classifying Time Independent Plane Symmetric Spacetime through Noether`s Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nazish%20Iftikhar">Nazish Iftikhar</a>, <a href="https://publications.waset.org/abstracts/search?q=Adil%20Jhangeer"> Adil Jhangeer</a>, <a href="https://publications.waset.org/abstracts/search?q=Tayyaba%20Naz"> Tayyaba Naz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The universe is expanding at an accelerated rate. Symmetries are useful in understanding universe’s behavior. Emmy Noether reported the relation between symmetries and conservation laws. These symmetries are known as Noether symmetries which correspond to a conserved quantity. In differential equations, conservation laws play an important role. Noether symmetries are helpful in modified theories of gravity. Time independent plane symmetric spacetime was classified by Noether`s theorem. By using Noether`s theorem, set of linear partial differential equations was obtained having A(r), B(r) and F(r) as unknown radial functions. The Lagrangian corresponding to considered spacetime in the Noether equation was used to get Noether operators. Different possibilities of radial functions were considered. Firstly, all functions were same. All the functions were considered as non-zero constant, linear, reciprocal and exponential respectively. Secondly, two functions were proportional to each other keeping third function different. Second case has four subcases in which four different relationships between A(r), B(r) and F(r) were discussed. In all cases, we obtained nontrivial Noether operators including gauge term. Conserved quantities for each Noether operators were also presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noether%20gauge%20symmetries" title="Noether gauge symmetries">Noether gauge symmetries</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20function" title=" radial function"> radial function</a>, <a href="https://publications.waset.org/abstracts/search?q=Noether%20operator" title=" Noether operator"> Noether operator</a>, <a href="https://publications.waset.org/abstracts/search?q=conserved%20quantities" title=" conserved quantities"> conserved quantities</a> </p> <a href="https://publications.waset.org/abstracts/71907/classifying-time-independent-plane-symmetric-spacetime-through-noethers-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71907.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">230</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28182</span> Discontinuous Spacetime with Vacuum Holes as Explanation for Gravitation, Quantum Mechanics and Teleportation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Constantin%20Z.%20Leshan">Constantin Z. Leshan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hole Vacuum theory is based on discontinuous spacetime that contains vacuum holes. Vacuum holes can explain gravitation, some laws of quantum mechanics and allow teleportation of matter. All massive bodies emit a flux of holes which curve the spacetime; if we increase the concentration of holes, it leads to length contraction and time dilation because the holes do not have the properties of extension and duration. In the limited case when space consists of holes only, the distance between every two points is equal to zero and time stops - outside of the Universe, the extension and duration properties do not exist. For this reason, the vacuum hole is the only particle in physics capable of describing gravitation using its own properties only. All microscopic particles must 'jump' continually and 'vibrate' due to the appearance of holes (impassable microscopic 'walls' in space), and it is the cause of the quantum behavior. Vacuum holes can explain the entanglement, non-locality, wave properties of matter, tunneling, uncertainty principle and so on. Particles do not have trajectories because spacetime is discontinuous and has impassable microscopic 'walls' due to the simple mechanical motion is impossible at small scale distances; it is impossible to 'trace' a straight line in the discontinuous spacetime because it contains the impassable holes. Spacetime 'boils' continually due to the appearance of the vacuum holes. For teleportation to be possible, we must send a body outside of the Universe by enveloping it with a closed surface consisting of vacuum holes. Since a material body cannot exist outside of the Universe, it reappears instantaneously in a random point of the Universe. Since a body disappears in one volume and reappears in another random volume without traversing the physical space between them, such a transportation method can be called teleportation (or Hole Teleportation). It is shown that Hole Teleportation does not violate causality and special relativity due to its random nature and other properties. Although Hole Teleportation has a random nature, it can be used for colonization of extrasolar planets by the help of the method called 'random jumps': after a large number of random teleportation jumps, there is a probability that the spaceship may appear near a habitable planet. We can create vacuum holes experimentally using the method proposed by Descartes: we must remove a body from the vessel without permitting another body to occupy this volume. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=border%20of%20the%20Universe" title="border of the Universe">border of the Universe</a>, <a href="https://publications.waset.org/abstracts/search?q=causality%20violation" title=" causality violation"> causality violation</a>, <a href="https://publications.waset.org/abstracts/search?q=perfect%20isolation" title=" perfect isolation"> perfect isolation</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20jumps" title=" quantum jumps"> quantum jumps</a> </p> <a href="https://publications.waset.org/abstracts/55259/discontinuous-spacetime-with-vacuum-holes-as-explanation-for-gravitation-quantum-mechanics-and-teleportation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55259.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">425</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28181</span> Improved K-Means Clustering Algorithm Using RHadoop with Combiner</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ji%20Eun%20Shin">Ji Eun Shin</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Hoon%20Lim"> Dong Hoon Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=combiner" title=" combiner"> combiner</a>, <a href="https://publications.waset.org/abstracts/search?q=K-means%20clustering" title=" K-means clustering"> K-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=RHadoop" title=" RHadoop"> RHadoop</a> </p> <a href="https://publications.waset.org/abstracts/41570/improved-k-means-clustering-algorithm-using-rhadoop-with-combiner" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41570.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">438</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28180</span> Analyzing Boson Star as a Candidate for Dark Galaxy Using ADM Formulation of General Relativity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aria%20Ratmandanu">Aria Ratmandanu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Boson stars can be viewed as zero temperature ground state, Bose-Einstein condensates, characterized by enormous occupation numbers. Time-dependent spherically symmetric spacetime can be a model of Boson Star. We use (3+1) split of Einstein equation (ADM formulation of general relativity) to solve Einstein field equation coupled to a complex scalar field (Einstein-Klein-Gordon Equation) on time-dependent spherically symmetric spacetime, We get the result that Boson stars are pulsating stars with the frequency of oscillation equal to its density. We search for interior solution of Boson stars and get the T.O.V. (Tollman-Oppenheimer-Volkoff) equation for Boson stars. Using T.O.V. equation, we get the equation of state and the relation between pressure and density, its total mass and along with its gravitational Mass. We found that the hypothetical particle Axion could form a Boson star with the size of a milky way galaxy and make it a candidate for a dark galaxy, (a galaxy that consists almost entirely of dark matter). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=axion" title="axion">axion</a>, <a href="https://publications.waset.org/abstracts/search?q=boson%20star" title=" boson star"> boson star</a>, <a href="https://publications.waset.org/abstracts/search?q=dark%20galaxy" title=" dark galaxy"> dark galaxy</a>, <a href="https://publications.waset.org/abstracts/search?q=time-dependent%20spherically%20symmetric%20spacetime" title=" time-dependent spherically symmetric spacetime"> time-dependent spherically symmetric spacetime</a> </p> <a href="https://publications.waset.org/abstracts/70005/analyzing-boson-star-as-a-candidate-for-dark-galaxy-using-adm-formulation-of-general-relativity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70005.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">244</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28179</span> Semi-Supervised Hierarchical Clustering Given a Reference Tree of Labeled Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Zhao">Ying Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingyan%20Bin"> Xingyan Bin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Semi-supervised clustering algorithms have been shown effective to improve clustering process with even limited supervision. However, semi-supervised hierarchical clustering remains challenging due to the complexities of expressing constraints for agglomerative clustering algorithms. This paper proposes novel semi-supervised agglomerative clustering algorithms to build a hierarchy based on a known reference tree. We prove that by enforcing distance constraints defined by a reference tree during the process of hierarchical clustering, the resultant tree is guaranteed to be consistent with the reference tree. We also propose a framework that allows the hierarchical tree generation be aware of levels of levels of the agglomerative tree under creation, so that metric weights can be learned and adopted at each level in a recursive fashion. The experimental evaluation shows that the additional cost of our contraint-based semi-supervised hierarchical clustering algorithm (HAC) is negligible, and our combined semi-supervised HAC algorithm outperforms the state-of-the-art algorithms on real-world datasets. The experiments also show that our proposed methods can improve clustering performance even with a small number of unevenly distributed labeled data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semi-supervised%20clustering" title="semi-supervised clustering">semi-supervised clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%0D%0Aagglomerative%20clustering" title=" hierarchical agglomerative clustering"> hierarchical agglomerative clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=reference%20trees" title=" reference trees"> reference trees</a>, <a href="https://publications.waset.org/abstracts/search?q=distance%20constraints" title=" distance constraints "> distance constraints </a> </p> <a href="https://publications.waset.org/abstracts/19478/semi-supervised-hierarchical-clustering-given-a-reference-tree-of-labeled-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19478.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">547</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28178</span> Pattern Recognition Using Feature Based Die-Map Clustering in the Semiconductor Manufacturing Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seung%20Hwan%20Park">Seung Hwan Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng-Sool%20Park"> Cheng-Sool Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20Seok%20Kim"> Jun Seok Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Youngji%20Yoo"> Youngji Yoo</a>, <a href="https://publications.waset.org/abstracts/search?q=Daewoong%20An"> Daewoong An</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun-Geol%20Baek"> Jun-Geol Baek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Depending on the big data analysis becomes important, yield prediction using data from the semiconductor process is essential. In general, yield prediction and analysis of the causes of the failure are closely related. The purpose of this study is to analyze pattern affects the final test results using a die map based clustering. Many researches have been conducted using die data from the semiconductor test process. However, analysis has limitation as the test data is less directly related to the final test results. Therefore, this study proposes a framework for analysis through clustering using more detailed data than existing die data. This study consists of three phases. In the first phase, die map is created through fail bit data in each sub-area of die. In the second phase, clustering using map data is performed. And the third stage is to find patterns that affect final test result. Finally, the proposed three steps are applied to actual industrial data and experimental results showed the potential field application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=die-map%20clustering" title="die-map clustering">die-map clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=semiconductor%20manufacturing%20process" title=" semiconductor manufacturing process"> semiconductor manufacturing process</a> </p> <a href="https://publications.waset.org/abstracts/5000/pattern-recognition-using-feature-based-die-map-clustering-in-the-semiconductor-manufacturing-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5000.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">402</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28177</span> ACOPIN: An ACO Algorithm with TSP Approach for Clustering Proteins in Protein Interaction Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamaludin%20Sallim">Jamaludin Sallim</a>, <a href="https://publications.waset.org/abstracts/search?q=Rozlina%20Mohamed"> Rozlina Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Roslina%20Abdul%20Hamid"> Roslina Abdul Hamid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we proposed an Ant Colony Optimization (ACO) algorithm together with Traveling Salesman Problem (TSP) approach to investigate the clustering problem in Protein Interaction Networks (PIN). We named this combination as ACOPIN. The purpose of this work is two-fold. First, to test the efficacy of ACO in clustering PIN and second, to propose the simple generalization of the ACO algorithm that might allow its application in clustering proteins in PIN. We split this paper to three main sections. First, we describe the PIN and clustering proteins in PIN. Second, we discuss the steps involved in each phase of ACO algorithm. Finally, we present some results of the investigation with the clustering patterns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20algorithm" title="ant colony optimization algorithm">ant colony optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=searching%20algorithm" title=" searching algorithm"> searching algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20functional%20module" title=" protein functional module"> protein functional module</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20interaction%20network" title=" protein interaction network "> protein interaction network </a> </p> <a href="https://publications.waset.org/abstracts/22367/acopin-an-aco-algorithm-with-tsp-approach-for-clustering-proteins-in-protein-interaction-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22367.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">612</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28176</span> Analysis of ECGs Survey Data by Applying Clustering Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irum%20Matloob">Irum Matloob</a>, <a href="https://publications.waset.org/abstracts/search?q=Shoab%20Ahmad%20Khan"> Shoab Ahmad Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahim%20Arif"> Fahim Arif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As Indo-pak has been the victim of heart diseases since many decades. Many surveys showed that percentage of cardiac patients is increasing in Pakistan day by day, and special attention is needed to pay on this issue. The framework is proposed for performing detailed analysis of ECG survey data which is conducted for measuring the prevalence of heart diseases statistics in Pakistan. The ECG survey data is evaluated or filtered by using automated Minnesota codes and only those ECGs are used for further analysis which is fulfilling the standardized conditions mentioned in the Minnesota codes. Then feature selection is performed by applying proposed algorithm based on discernibility matrix, for selecting relevant features from the database. Clustering is performed for exposing natural clusters from the ECG survey data by applying spectral clustering algorithm using fuzzy c means algorithm. The hidden patterns and interesting relationships which have been exposed after this analysis are useful for further detailed analysis and for many other multiple purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arrhythmias" title="arrhythmias">arrhythmias</a>, <a href="https://publications.waset.org/abstracts/search?q=centroids" title=" centroids"> centroids</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=discernibility%20matrix" title=" discernibility matrix"> discernibility matrix</a> </p> <a href="https://publications.waset.org/abstracts/32782/analysis-of-ecgs-survey-data-by-applying-clustering-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32782.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">351</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28175</span> Spectral Clustering for Manufacturing Cell Formation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yessica%20Nataliani">Yessica Nataliani</a>, <a href="https://publications.waset.org/abstracts/search?q=Miin-Shen%20Yang"> Miin-Shen Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cell formation (CF) is an important step in group technology. It is used in designing cellular manufacturing systems using similarities between parts in relation to machines so that it can identify part families and machine groups. There are many CF methods in the literature, but there is less spectral clustering used in CF. In this paper, we propose a spectral clustering algorithm for machine-part CF. Some experimental examples are used to illustrate its efficiency. Overall, the spectral clustering algorithm can be used in CF with a wide variety of machine/part matrices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=group%20technology" title="group technology">group technology</a>, <a href="https://publications.waset.org/abstracts/search?q=cell%20formation" title=" cell formation"> cell formation</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20clustering" title=" spectral clustering"> spectral clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=grouping%20efficiency" title=" grouping efficiency"> grouping efficiency</a> </p> <a href="https://publications.waset.org/abstracts/72294/spectral-clustering-for-manufacturing-cell-formation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72294.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">408</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28174</span> Anomaly Detection Based Fuzzy K-Mode Clustering for Categorical Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Murat%20Yazici">Murat Yazici</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20k-mode%20clustering" title="fuzzy k-mode clustering">fuzzy k-mode clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a>, <a href="https://publications.waset.org/abstracts/search?q=categorical%20data" title=" categorical data"> categorical data</a> </p> <a href="https://publications.waset.org/abstracts/185305/anomaly-detection-based-fuzzy-k-mode-clustering-for-categorical-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185305.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">54</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28173</span> Interpretation and Clustering Framework for Analyzing ECG Survey Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irum%20Matloob">Irum Matloob</a>, <a href="https://publications.waset.org/abstracts/search?q=Shoab%20Ahmad%20Khan"> Shoab Ahmad Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahim%20Arif"> Fahim Arif </a> </p> <p class="card-text"><strong>Abstract:</strong></p> As Indo-Pak has been the victim of heart diseases since many decades. Many surveys showed that percentage of cardiac patients is increasing in Pakistan day by day, and special attention is needed to pay on this issue. The framework is proposed for performing detailed analysis of ECG survey data which is conducted for measuring prevalence of heart diseases statistics in Pakistan. The ECG survey data is evaluated or filtered by using automated Minnesota codes and only those ECGs are used for further analysis which is fulfilling the standardized conditions mentioned in the Minnesota codes. Then feature selection is performed by applying proposed algorithm based on discernibility matrix, for selecting relevant features from the database. Clustering is performed for exposing natural clusters from the ECG survey data by applying spectral clustering algorithm using fuzzy c means algorithm. The hidden patterns and interesting relationships which have been exposed after this analysis are useful for further detailed analysis and for many other multiple purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arrhythmias" title="arrhythmias">arrhythmias</a>, <a href="https://publications.waset.org/abstracts/search?q=centroids" title=" centroids"> centroids</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=discernibility%20matrix" title=" discernibility matrix"> discernibility matrix</a> </p> <a href="https://publications.waset.org/abstracts/16194/interpretation-and-clustering-framework-for-analyzing-ecg-survey-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16194.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">470</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28172</span> GCM Based Fuzzy Clustering to Identify Homogeneous Climatic Regions of North-East India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arup%20K.%20Sarma">Arup K. Sarma</a>, <a href="https://publications.waset.org/abstracts/search?q=Jayshree%20Hazarika"> Jayshree Hazarika</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The North-eastern part of India, which receives heavier rainfall than other parts of the subcontinent, is of great concern now-a-days with regard to climate change. High intensity rainfall for short duration and longer dry spell, occurring due to impact of climate change, affects river morphology too. In the present study, an attempt is made to delineate the North-Eastern region of India into some homogeneous clusters based on the Fuzzy Clustering concept and to compare the resulting clusters obtained by using conventional methods and non conventional methods of clustering. The concept of clustering is adapted in view of the fact that, impact of climate change can be studied in a homogeneous region without much variation, which can be helpful in studies related to water resources planning and management. 10 IMD (Indian Meteorological Department) stations, situated in various regions of the North-east, have been selected for making the clusters. The results of the Fuzzy C-Means (FCM) analysis show different clustering patterns for different conditions. From the analysis and comparison it can be concluded that non conventional method of using GCM data is somehow giving better results than the others. However, further analysis can be done by taking daily data instead of monthly means to reduce the effect of standardization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title="climate change">climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=conventional%20and%20nonconventional%20methods%20of%20clustering" title=" conventional and nonconventional methods of clustering"> conventional and nonconventional methods of clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=FCM%20analysis" title=" FCM analysis"> FCM analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=homogeneous%20regions" title=" homogeneous regions"> homogeneous regions</a> </p> <a href="https://publications.waset.org/abstracts/16059/gcm-based-fuzzy-clustering-to-identify-homogeneous-climatic-regions-of-north-east-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16059.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28171</span> Sales Patterns Clustering Analysis on Seasonal Product Sales Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soojin%20Kim">Soojin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiwon%20Yang"> Jiwon Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungzoon%20Cho"> Sungzoon Cho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a seasonal product is only in demand for a short time, inventory management is critical to profits. Both markdowns and stockouts decrease the return on perishable products; therefore, researchers have been interested in the distribution of seasonal products with the aim of maximizing profits. In this study, we propose a data-driven seasonal product sales pattern analysis method for individual retail outlets based on observed sales data clustering; the proposed method helps in determining distribution strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution" title=" distribution"> distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20pattern" title=" sales pattern"> sales pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=seasonal%20product" title=" seasonal product"> seasonal product</a> </p> <a href="https://publications.waset.org/abstracts/22411/sales-patterns-clustering-analysis-on-seasonal-product-sales-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22411.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">595</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28170</span> A Non-parametric Clustering Approach for Multivariate Geostatistical Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Francky%20Fouedjio">Francky Fouedjio</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multivariate geostatistical data have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in some sense. Spatially contiguous clusters can significantly improve the interpretation that turns the resulting clusters into meaningful geographical subregions. In this paper, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the spatial dependence structure of data. It integrates existing methods to find the optimal cluster number and to evaluate the contribution of variables to the clustering. The capability of the proposed approach to provide spatially compact, connected and meaningful clusters is assessed using bivariate synthetic dataset and multivariate geochemical dataset. The proposed clustering method gives satisfactory results compared to other similar geostatistical clustering methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=geostatistics" title=" geostatistics"> geostatistics</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20data" title=" multivariate data"> multivariate data</a>, <a href="https://publications.waset.org/abstracts/search?q=non-parametric" title=" non-parametric"> non-parametric</a> </p> <a href="https://publications.waset.org/abstracts/46870/a-non-parametric-clustering-approach-for-multivariate-geostatistical-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46870.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">477</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28169</span> Investigation of Clustering Algorithms Used in Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naim%20Karasekreter">Naim Karasekreter</a>, <a href="https://publications.waset.org/abstracts/search?q=Ugur%20Fidan"> Ugur Fidan</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatih%20Basciftci"> Fatih Basciftci</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless sensor networks are networks in which more than one sensor node is organized among themselves. The working principle is based on the transfer of the sensed data over the other nodes in the network to the central station. Wireless sensor networks concentrate on routing algorithms, energy efficiency and clustering algorithms. In the clustering method, the nodes in the network are divided into clusters using different parameters and the most suitable cluster head is selected from among them. The data to be sent to the center is sent per cluster, and the cluster head is transmitted to the center. With this method, the network traffic is reduced and the energy efficiency of the nodes is increased. In this study, clustering algorithms were examined in terms of clustering performances and cluster head selection characteristics to try to identify weak and strong sides. This work is supported by the Project 17.Kariyer.123 of Afyon Kocatepe University BAP Commission. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks%20%28WSN%29" title="wireless sensor networks (WSN)">wireless sensor networks (WSN)</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithm" title=" clustering algorithm"> clustering algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20head" title=" cluster head"> cluster head</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a> </p> <a href="https://publications.waset.org/abstracts/78846/investigation-of-clustering-algorithms-used-in-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78846.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">513</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28168</span> A Comparative Study of Multi-SOM Algorithms for Determining the Optimal Number of Clusters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Im%C3%A8n%20Khanchouch">Imèn Khanchouch</a>, <a href="https://publications.waset.org/abstracts/search?q=Malika%20Charrad"> Malika Charrad</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Limam"> Mohamed Limam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The interpretation of the quality of clusters and the determination of the optimal number of clusters is still a crucial problem in clustering. We focus in this paper on multi-SOM clustering method which overcomes the problem of extracting the number of clusters from the SOM map through the use of a clustering validity index. We then tested multi-SOM using real and artificial data sets with different evaluation criteria not used previously such as Davies Bouldin index, Dunn index and silhouette index. The developed multi-SOM algorithm is compared to k-means and Birch methods. Results show that it is more efficient than classical clustering methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=SOM" title=" SOM"> SOM</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-SOM" title=" multi-SOM"> multi-SOM</a>, <a href="https://publications.waset.org/abstracts/search?q=DB%20index" title=" DB index"> DB index</a>, <a href="https://publications.waset.org/abstracts/search?q=Dunn%20index" title=" Dunn index"> Dunn index</a>, <a href="https://publications.waset.org/abstracts/search?q=silhouette%20index" title=" silhouette index"> silhouette index</a> </p> <a href="https://publications.waset.org/abstracts/17422/a-comparative-study-of-multi-som-algorithms-for-determining-the-optimal-number-of-clusters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17422.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">599</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=space-time%20clustering%20analysis&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=space-time%20clustering%20analysis&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=space-time%20clustering%20analysis&page=4">4</a></li> <li class="page-item"><a class="page-link" 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