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clustering</title> <meta name="description" content="Search results for: fuzzy k-mode clustering"> <meta name="keywords" content="fuzzy k-mode clustering"> <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> <button class="d-block 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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="fuzzy k-mode clustering"> <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> 1253</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: fuzzy k-mode clustering</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1253</span> A Fuzzy Kernel K-Medoids Algorithm for Clustering Uncertain Data Objects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Behnam%20Tavakkol">Behnam Tavakkol</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters. <p class="card-text"><strong>Keywords:</strong> <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=fuzzy%20methods" title=" fuzzy methods"> fuzzy methods</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20k-medoids" title=" kernel k-medoids"> kernel k-medoids</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertain%20data" title=" uncertain data"> uncertain data</a> </p> <a href="https://publications.waset.org/abstracts/123501/a-fuzzy-kernel-k-medoids-algorithm-for-clustering-uncertain-data-objects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123501.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">215</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">1252</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">1251</span> Knowledge Representation Based on Interval Type-2 CFCM Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lee%20Myung-Won">Lee Myung-Won</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwak%20Keun-Chang"> Kwak Keun-Chang </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IT2-FCM" title="IT2-FCM">IT2-FCM</a>, <a href="https://publications.waset.org/abstracts/search?q=IT2-CFCM" title=" IT2-CFCM"> IT2-CFCM</a>, <a href="https://publications.waset.org/abstracts/search?q=context-based%20fuzzy%20clustering" title=" context-based fuzzy clustering"> context-based fuzzy clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20network" title=" adaptive neuro-fuzzy network"> adaptive neuro-fuzzy network</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20representation" title=" knowledge representation"> knowledge representation</a> </p> <a href="https://publications.waset.org/abstracts/28945/knowledge-representation-based-on-interval-type-2-cfcm-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28945.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">321</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">1250</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">53</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">1249</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">1248</span> Using Genetic Algorithms and Rough Set Based Fuzzy K-Modes to Improve Centroid Model Clustering Performance on Categorical Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rishabh%20Srivastav">Rishabh Srivastav</a>, <a href="https://publications.waset.org/abstracts/search?q=Divyam%20%20Sharma"> Divyam Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose an algorithm to cluster categorical data named as ‘Genetic algorithm initialized rough set based fuzzy K-Modes for categorical data’. We propose an amalgamation of the simple K-modes algorithm, the Rough and Fuzzy set based K-modes and the Genetic Algorithm to form a new algorithm,which we hypothesise, will provide better Centroid Model clustering results, than existing standard algorithms. In the proposed algorithm, the initialization and updation of modes is done by the use of genetic algorithms while the membership values are calculated using the rough set and fuzzy logic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=categorical%20data" title="categorical data">categorical data</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=K%20modes%20clustering" title=" K modes clustering"> K modes clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=rough%20sets" title=" rough sets"> rough sets</a> </p> <a href="https://publications.waset.org/abstracts/128558/using-genetic-algorithms-and-rough-set-based-fuzzy-k-modes-to-improve-centroid-model-clustering-performance-on-categorical-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128558.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">246</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">1247</span> Application of Fuzzy Clustering on Classification Agile Supply Chain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamidreza%20Fallah%20Lajimi">Hamidreza Fallah Lajimi </a>, <a href="https://publications.waset.org/abstracts/search?q=Elham%20Karami"> Elham Karami</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatemeh%20Ali%20nasab"> Fatemeh Ali nasab</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Mahdavikia"> Mostafa Mahdavikia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Being responsive is an increasingly important skill for firms in today’s global economy; thus firms must be agile. Naturally, it follows that an organization’s agility depends on its supply chain being agile. However, achieving supply chain agility is a function of other abilities within the organization. This paper analyses results from a survey of 71 Iran manufacturing companies in order to identify some of the factors for agile organizations in managing their supply chains. Then we classification this company in four cluster with fuzzy c-mean technique and with four validations functional determine automatically the optimal number of clusters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agile%20supply%20chain" title="agile supply chain">agile supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</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/28115/application-of-fuzzy-clustering-on-classification-agile-supply-chain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28115.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">474</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">1246</span> Intelligent Recognition of Diabetes Disease via FCM Based Attribute Weighting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kemal%20Polat">Kemal Polat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20C-means%20clustering" title="fuzzy C-means clustering">fuzzy C-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20C-means%20clustering%20based%20attribute%20weighting" title=" fuzzy C-means clustering based attribute weighting"> fuzzy C-means clustering based attribute weighting</a>, <a href="https://publications.waset.org/abstracts/search?q=Pima%20Indians%20diabetes" title=" Pima Indians diabetes"> Pima Indians diabetes</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/46171/intelligent-recognition-of-diabetes-disease-via-fcm-based-attribute-weighting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46171.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">413</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">1245</span> Application of Fuzzy Clustering on Classification Agile Supply Chain Firms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamidreza%20Fallah%20Lajimi">Hamidreza Fallah Lajimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Elham%20Karami"> Elham Karami</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Arab"> Alireza Arab</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatemeh%20Alinasab"> Fatemeh Alinasab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Being responsive is an increasingly important skill for firms in today’s global economy; thus firms must be agile. Naturally, it follows that an organization’s agility depends on its supply chain being agile. However, achieving supply chain agility is a function of other abilities within the organization. This paper analyses results from a survey of 71 Iran manufacturing companies in order to identify some of the factors for agile organizations in managing their supply chains. Then we classification this company in four cluster with fuzzy c-mean technique and with Four validations functional determine automatically the optimal number of clusters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agile%20supply%20chain" title="agile supply chain">agile supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <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=business%20engineering" title=" business engineering"> business engineering</a> </p> <a href="https://publications.waset.org/abstracts/28866/application-of-fuzzy-clustering-on-classification-agile-supply-chain-firms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28866.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">712</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">1244</span> A Relative Entropy Regularization Approach for Fuzzy C-Means Clustering Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ouafa%20Amira">Ouafa Amira</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiangshe%20Zhang"> Jiangshe Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy. <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=fuzzy%20c-means" title=" fuzzy c-means"> fuzzy c-means</a>, <a href="https://publications.waset.org/abstracts/search?q=regularization" title=" regularization"> regularization</a>, <a href="https://publications.waset.org/abstracts/search?q=relative%20entropy" title=" relative entropy"> relative entropy</a> </p> <a href="https://publications.waset.org/abstracts/96361/a-relative-entropy-regularization-approach-for-fuzzy-c-means-clustering-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96361.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">259</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">1243</span> An Extraction of Cancer Region from MR Images Using Fuzzy Clustering Means and Morphological Operations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramandeep%20Kaur">Ramandeep Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurjit%20Singh%20Bhathal"> Gurjit Singh Bhathal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cancer diagnosis is very difficult task. Magnetic resonance imaging (MRI) scan is used to produce image of any part of the body and provides an efficient way for diagnosis of cancer or tumor. In existing method, fuzzy clustering mean (FCM) is used for the diagnosis of the tumor. In the proposed method FCM is used to diagnose the cancer of the foot. FCM finds the centroids of the clusters of the foot cancer obtained from MRI images. FCM thresholding result shows the extract region of the cancer. Morphological operations are applied to get extracted region of cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging%20%28MRI%29" title="magnetic resonance imaging (MRI)">magnetic resonance imaging (MRI)</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20C%20mean%20clustering" title=" fuzzy C mean clustering"> fuzzy C mean clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20operations" title=" morphological operations"> morphological operations</a> </p> <a href="https://publications.waset.org/abstracts/5937/an-extraction-of-cancer-region-from-mr-images-using-fuzzy-clustering-means-and-morphological-operations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5937.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">1242</span> Water Detection in Aerial Images Using Fuzzy Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Caio%20Marcelo%20Nunes">Caio Marcelo Nunes</a>, <a href="https://publications.waset.org/abstracts/search?q=Anderson%20da%20Silva%20Soares"> Anderson da Silva Soares</a>, <a href="https://publications.waset.org/abstracts/search?q=Gustavo%20Teodoro%20Laureano"> Gustavo Teodoro Laureano</a>, <a href="https://publications.waset.org/abstracts/search?q=Clarimar%20Jose%20Coelho"> Clarimar Jose Coelho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a methodology to pixel recognition in aerial images using fuzzy $c$-means algorithm. This algorithm is a alternative to recognize areas considering uncertainties and inaccuracies. Traditional clustering technics are used in recognizing of multispectral images of earth's surface. This technics recognize well-defined borders that can be easily discretized. However, in the real world there are many areas with uncertainties and inaccuracies which can be mapped by clustering algorithms that use fuzzy sets. The methodology presents in this work is applied to multispectral images obtained from Landsat-5/TM satellite. The pixels are joined using the $c$-means algorithm. After, a classification process identify the types of surface according the patterns obtained from spectral response of image surface. The classes considered are, exposed soil, moist soil, vegetation, turbid water and clean water. The results obtained shows that the fuzzy clustering identify the real type of the earth's surface. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aerial%20images" title="aerial images">aerial images</a>, <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=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/30574/water-detection-in-aerial-images-using-fuzzy-sets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30574.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">482</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">1241</span> Performance Analysis of Deterministic Stable Election Protocol Using Fuzzy Logic in Wireless Sensor Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sumanpreet%20Kaur">Sumanpreet Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Harjit%20Pal%20Singh"> Harjit Pal Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Vikas%20Khullar"> Vikas Khullar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Wireless Sensor Network (WSN), the sensor containing motes (nodes) incorporate batteries that can lament at some extent. To upgrade the energy utilization, clustering is one of the prototypical approaches for split sensor motes into a number of clusters where one mote (also called as node) proceeds as a Cluster Head (CH). CH selection is one of the optimization techniques for enlarging stability and network lifespan. Deterministic Stable Election Protocol (DSEP) is an effectual clustering protocol that makes use of three kinds of nodes with dissimilar residual energy for CH election. Fuzzy Logic technology is used to expand energy level of DSEP protocol by using fuzzy inference system. This paper presents protocol DSEP using Fuzzy Logic (DSEP-FL) CH by taking into account four linguistic variables such as energy, concentration, centrality and distance to base station. Simulation results show that our proposed method gives more effective results in term of a lifespan of network and stability as compared to the performance of other clustering protocols. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DSEP" title="DSEP">DSEP</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20model" title=" energy model"> energy model</a>, <a href="https://publications.waset.org/abstracts/search?q=WSN" title=" WSN"> WSN</a> </p> <a href="https://publications.waset.org/abstracts/73747/performance-analysis-of-deterministic-stable-election-protocol-using-fuzzy-logic-in-wireless-sensor-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73747.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">207</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">1240</span> Multi-Cluster Overlapping K-Means Extension Algorithm (MCOKE)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Said%20Baadel">Said Baadel</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadi%20Thabtah"> Fadi Thabtah</a>, <a href="https://publications.waset.org/abstracts/search?q=Joan%20Lu"> Joan Lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard-partitioning techniques where each object is assigned to one cluster. In this paper, we propose an overlapping algorithm MCOKE which allows objects to belong to one or more clusters. The algorithm is different from fuzzy clustering techniques because objects that overlap are assigned a membership value of 1 (one) as opposed to a fuzzy membership degree. The algorithm is also different from other overlapping algorithms that require a similarity threshold to be defined as a priority which can be difficult to determine by novice users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=MCOKE" title=" MCOKE"> MCOKE</a>, <a href="https://publications.waset.org/abstracts/search?q=overlapping" title=" overlapping"> overlapping</a> </p> <a href="https://publications.waset.org/abstracts/18638/multi-cluster-overlapping-k-means-extension-algorithm-mcoke" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18638.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">575</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">1239</span> Fuzzy Time Series Forecasting Based on Fuzzy Logical Relationships, PSO Technique, and Automatic Clustering Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20M.%20Kamrul%20Islam">A. K. M. Kamrul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhamid%20Bouchachia"> Abdelhamid Bouchachia</a>, <a href="https://publications.waset.org/abstracts/search?q=Suang%20Cang"> Suang Cang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongnian%20Yu"> Hongnian Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20time%20series%20%28fts%29" title="fuzzy time series (fts)">fuzzy time series (fts)</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</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=hybrid%20forecasting%20model" title=" hybrid forecasting model"> hybrid forecasting model</a> </p> <a href="https://publications.waset.org/abstracts/51515/fuzzy-time-series-forecasting-based-on-fuzzy-logical-relationships-pso-technique-and-automatic-clustering-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51515.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">250</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">1238</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">1237</span> A Hybrid Fuzzy Clustering Approach for Fertile and Unfertile Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shima%20Soltanzadeh">Shima Soltanzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hosain%20Fazel%20Zarandi"> Mohammad Hosain Fazel Zarandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mojtaba%20Barzegar%20Astanjin"> Mojtaba Barzegar Astanjin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c-means" title=" fuzzy c-means"> fuzzy c-means</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Naive%20Bayesian" title=" Naive Bayesian"> Naive Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=ROC%20curve" title=" ROC curve"> ROC curve</a> </p> <a href="https://publications.waset.org/abstracts/50838/a-hybrid-fuzzy-clustering-approach-for-fertile-and-unfertile-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50838.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">336</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">1236</span> Improved Qualitative Modeling of the Magnetization Curve B(H) of the Ferromagnetic Materials for a Transformer Used in the Power Supply for Magnetron</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Bassoui">M. Bassoui</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Ferfra"> M. Ferfra</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Chrayagne"> M. Chrayagne</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a qualitative modeling for the nonlinear B-H curve of the saturable magnetic materials for a transformer with shunts used in the power supply for the magnetron. This power supply is composed of a single phase leakage flux transformer supplying a cell composed of a capacitor and a diode, which double the voltage and stabilize the current, and a single magnetron at the output of the cell. A procedure consisting of a fuzzy clustering method and a rule processing algorithm is then employed for processing the constructed fuzzy modeling rules to extract the qualitative properties of the curve. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=B%28H%29%20curve" title="B(H) curve">B(H) curve</a>, <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=magnetron" title=" magnetron"> magnetron</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20supply" title=" power supply"> power supply</a> </p> <a href="https://publications.waset.org/abstracts/59538/improved-qualitative-modeling-of-the-magnetization-curve-bh-of-the-ferromagnetic-materials-for-a-transformer-used-in-the-power-supply-for-magnetron" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59538.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">236</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">1235</span> Approach Based on Fuzzy C-Means for Band Selection in Hyperspectral Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diego%20Saqui">Diego Saqui</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20H.%20Saito"> José H. Saito</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20R.%20Campos"> José R. Campos</a>, <a href="https://publications.waset.org/abstracts/search?q=L%C3%BAcio%20A.%20de%20C.%20Jorge"> Lúcio A. de C. Jorge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hyperspectral images and remote sensing are important for many applications. A problem in the use of these images is the high volume of data to be processed, stored and transferred. Dimensionality reduction techniques can be used to reduce the volume of data. In this paper, an approach to band selection based on clustering algorithms is presented. This approach allows to reduce the volume of data. The proposed structure is based on Fuzzy C-Means (or K-Means) and NWHFC algorithms. New attributes in relation to other studies in the literature, such as kurtosis and low correlation, are also considered. A comparison of the results of the approach using the Fuzzy C-Means and K-Means with different attributes is performed. The use of both algorithms show similar good results but, particularly when used attributes variance and kurtosis in the clustering process, however applicable in hyperspectral images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=band%20selection" title="band selection">band selection</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c-means" title=" fuzzy c-means"> fuzzy c-means</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20image" title=" hyperspectral image"> hyperspectral image</a> </p> <a href="https://publications.waset.org/abstracts/50614/approach-based-on-fuzzy-c-means-for-band-selection-in-hyperspectral-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50614.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">407</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">1234</span> Fuzzy Ideal Topological Spaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Koam">Ali Koam</a>, <a href="https://publications.waset.org/abstracts/search?q=Ismail%20Ibedou"> Ismail Ibedou</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20E.%20Abbas"> S. E. Abbas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, it is introduced the notion of r-fuzzy ideal separation axioms Tᵢi = 0; 1; 2 based on a fuzzy ideal I on a fuzzy topological space (X; τ). An r-fuzzy ideal connectedness related to the fuzzy ideal I is introduced which has relations with a previous r-fuzzy fuzzy connectedness. An r-fuzzy ideal compactness related to Ι is introduced which has also relations with many other types of fuzzy compactness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20ideal" title="fuzzy ideal">fuzzy ideal</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20separation%20axioms" title=" fuzzy separation axioms"> fuzzy separation axioms</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20compactness" title=" fuzzy compactness"> fuzzy compactness</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20connectedness" title=" fuzzy connectedness"> fuzzy connectedness</a> </p> <a href="https://publications.waset.org/abstracts/101746/fuzzy-ideal-topological-spaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101746.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">266</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">1233</span> AM/E/c Queuing Hub Maximal Covering Location Model with Fuzzy Parameter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Fazel%20Zarandi">M. H. Fazel Zarandi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Moshahedi"> N. Moshahedi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The hub location problem appears in a variety of applications such as medical centers, firefighting facilities, cargo delivery systems and telecommunication network design. The location of service centers has a strong influence on the congestion at each of them, and, consequently, on the quality of service. This paper presents a fuzzy maximal hub covering location problem (FMCHLP) in which travel costs between any pair of nodes is considered as a fuzzy variable. In order to consider the quality of service, we model each hub as a queue. Arrival rate follows Poisson distribution and service rate follows Erlang distribution. In this paper, at first, a nonlinear mathematical programming model is presented. Then, we convert it to the linear one. We solved the linear model using GAMS software up to 25 nodes and for large sizes due to the complexity of hub covering location problems, and simulated annealing algorithm is developed to solve and test the model. Also, we used possibilistic c-means clustering method in order to find an initial solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20modeling" title="fuzzy modeling">fuzzy modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=location" title=" location"> location</a>, <a href="https://publications.waset.org/abstracts/search?q=possibilistic%20clustering" title=" possibilistic clustering"> possibilistic clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=queuing" title=" queuing"> queuing</a> </p> <a href="https://publications.waset.org/abstracts/49645/amec-queuing-hub-maximal-covering-location-model-with-fuzzy-parameter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49645.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">393</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">1232</span> Clustering Based Level Set Evaluation for Low Contrast Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bikshalu%20Kalagadda">Bikshalu Kalagadda</a>, <a href="https://publications.waset.org/abstracts/search?q=Srikanth%20Rangu"> Srikanth Rangu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=segmentation" title="segmentation">segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=level%20set%20function" title=" level set function"> level set function</a>, <a href="https://publications.waset.org/abstracts/search?q=re-initialization" title=" re-initialization"> re-initialization</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20fuzzy" title=" Kernel fuzzy"> Kernel fuzzy</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20optimization" title=" swarm optimization"> swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/65723/clustering-based-level-set-evaluation-for-low-contrast-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65723.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">352</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">1231</span> Progressive Multimedia Collection Structuring via Scene Linking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aman%20Berhe">Aman Berhe</a>, <a href="https://publications.waset.org/abstracts/search?q=Camille%20Guinaudeau"> Camille Guinaudeau</a>, <a href="https://publications.waset.org/abstracts/search?q=Claude%20Barras"> Claude Barras</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to facilitate information seeking in large collections of multimedia documents with long and progressive content (such as broadcast news or TV series), one can extract the semantic links that exist between semantically coherent parts of documents, i.e., scenes. The links can then create a coherent collection of scenes from which it is easier to perform content analysis, topic extraction, or information retrieval. In this paper, we focus on TV series structuring and propose two approaches for scene linking at different levels of granularity (episode and season): a fuzzy online clustering technique and a graph-based community detection algorithm. When evaluated on the two first seasons of the TV series Game of Thrones, we found that the fuzzy online clustering approach performed better compared to graph-based community detection at the episode level, while graph-based approaches show better performance at the season level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multimedia%20collection%20structuring" title="multimedia collection structuring">multimedia collection structuring</a>, <a href="https://publications.waset.org/abstracts/search?q=progressive%20content" title=" progressive content"> progressive content</a>, <a href="https://publications.waset.org/abstracts/search?q=scene%20linking" title=" scene linking"> scene linking</a>, <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=community%20detection" title=" community detection"> community detection</a> </p> <a href="https://publications.waset.org/abstracts/153764/progressive-multimedia-collection-structuring-via-scene-linking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153764.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">99</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">1230</span> Design of a Fuzzy Luenberger Observer for Fault Nonlinear System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Bekaik">Mounir Bekaik</a>, <a href="https://publications.waset.org/abstracts/search?q=Messaoud%20Ramdani"> Messaoud Ramdani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present in this work a new technique of stabilization for fault nonlinear systems. The approach we adopt focus on a fuzzy Luenverger observer. The T-S approximation of the nonlinear observer is based on fuzzy C-Means clustering algorithm to find local linear subsystems. The MOESP identification approach was applied to design an empirical model describing the subsystems state variables. The gain of the observer is given by the minimization of the estimation error through Lyapunov-krasovskii functional and LMI approach. We consider a three tank hydraulic system for an illustrative example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20system" title="nonlinear system">nonlinear system</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy" title=" fuzzy"> fuzzy</a>, <a href="https://publications.waset.org/abstracts/search?q=faults" title=" faults"> faults</a>, <a href="https://publications.waset.org/abstracts/search?q=TS" title=" TS"> TS</a>, <a href="https://publications.waset.org/abstracts/search?q=Lyapunov-Krasovskii" title=" Lyapunov-Krasovskii"> Lyapunov-Krasovskii</a>, <a href="https://publications.waset.org/abstracts/search?q=observer" title=" observer"> observer</a> </p> <a href="https://publications.waset.org/abstracts/47230/design-of-a-fuzzy-luenberger-observer-for-fault-nonlinear-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47230.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">331</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">1229</span> Chemical Reaction Algorithm for Expectation Maximization Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Ni">Li Ni</a>, <a href="https://publications.waset.org/abstracts/search?q=Pen%20ManMan"> Pen ManMan</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20KenLi"> Li KenLi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chemical%20reaction%20optimization" title="chemical reaction optimization">chemical reaction optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=expection%20maimization" title=" expection maimization"> expection maimization</a>, <a href="https://publications.waset.org/abstracts/search?q=initia" title=" initia"> initia</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20function%20clustering" title=" objective function clustering"> objective function clustering</a> </p> <a href="https://publications.waset.org/abstracts/54706/chemical-reaction-algorithm-for-expectation-maximization-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54706.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">713</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">1228</span> From Type-I to Type-II Fuzzy System Modeling for Diagnosis of Hepatitis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahabeddin%20Sotudian">Shahabeddin Sotudian</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Fazel%20Zarandi"> M. H. Fazel Zarandi</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20B.%20Turksen"> I. B. Turksen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hepatitis%20disease" title="hepatitis disease">hepatitis disease</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20diagnosis" title=" medical diagnosis"> medical diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=type-I%20fuzzy%20logic" title=" type-I fuzzy logic"> type-I fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=type-II%20fuzzy%20logic" title=" type-II fuzzy logic"> type-II fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a> </p> <a href="https://publications.waset.org/abstracts/49654/from-type-i-to-type-ii-fuzzy-system-modeling-for-diagnosis-of-hepatitis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49654.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">306</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">1227</span> Liver Lesion Extraction with Fuzzy Thresholding in Contrast Enhanced Ultrasound Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abder-Rahman%20Ali">Abder-Rahman Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Ad%C3%A9la%C3%AFde%20Albouy-Kissi"> Adélaïde Albouy-Kissi</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20Grand-Brochier"> Manuel Grand-Brochier</a>, <a href="https://publications.waset.org/abstracts/search?q=Viviane%20Ladan-Marcus"> Viviane Ladan-Marcus</a>, <a href="https://publications.waset.org/abstracts/search?q=Christine%20Hoeffl"> Christine Hoeffl</a>, <a href="https://publications.waset.org/abstracts/search?q=Claude%20Marcus"> Claude Marcus</a>, <a href="https://publications.waset.org/abstracts/search?q=Antoine%20Vacavant"> Antoine Vacavant</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Yves%20Boire"> Jean-Yves Boire</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new segmentation approach for focal liver lesions in contrast enhanced ultrasound imaging. This approach, based on a two-cluster Fuzzy C-Means methodology, considers type-II fuzzy sets to handle uncertainty due to the image modality (presence of speckle noise, low contrast, etc.), and to calculate the optimum inter-cluster threshold. Fine boundaries are detected by a local recursive merging of ambiguous pixels. The method has been tested on a representative database. Compared to both Otsu and type-I Fuzzy C-Means techniques, the proposed method significantly reduces the segmentation errors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=defuzzification" title="defuzzification">defuzzification</a>, <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=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=type-II%20fuzzy%20sets" title=" type-II fuzzy sets"> type-II fuzzy sets</a> </p> <a href="https://publications.waset.org/abstracts/32293/liver-lesion-extraction-with-fuzzy-thresholding-in-contrast-enhanced-ultrasound-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32293.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">485</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">1226</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">1225</span> Sensitivity Analysis in Fuzzy Linear Programming Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Nasseri">S. H. Nasseri</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ebrahimnejad"> A. Ebrahimnejad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fuzzy set theory has been applied to many fields, such as operations research, control theory, and management sciences. In this paper, we consider two classes of fuzzy linear programming (FLP) problems: Fuzzy number linear programming and linear programming with trapezoidal fuzzy variables problems. We state our recently established results and develop fuzzy primal simplex algorithms for solving these problems. Finally, we give illustrative examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20linear%20programming" title="fuzzy linear programming">fuzzy linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20numbers" title=" fuzzy numbers"> fuzzy numbers</a>, <a href="https://publications.waset.org/abstracts/search?q=duality" title=" duality"> duality</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a> </p> <a href="https://publications.waset.org/abstracts/16916/sensitivity-analysis-in-fuzzy-linear-programming-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16916.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">565</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">1224</span> Automatic Detection of Proliferative Cells in Immunohistochemically Images of Meningioma Using Fuzzy C-Means Clustering and HSV Color Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Anari">Vahid Anari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mina%20Bakhshi"> Mina Bakhshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Visual search and identification of immunohistochemically stained tissue of meningioma was performed manually in pathologic laboratories to detect and diagnose the cancers type of meningioma. This task is very tedious and time-consuming. Moreover, because of cell's complex nature, it still remains a challenging task to segment cells from its background and analyze them automatically. In this paper, we develop and test a computerized scheme that can automatically identify cells in microscopic images of meningioma and classify them into positive (proliferative) and negative (normal) cells. Dataset including 150 images are used to test the scheme. The scheme uses Fuzzy C-means algorithm as a color clustering method based on perceptually uniform hue, saturation, value (HSV) color space. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=positive%20cell" title="positive cell">positive cell</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20segmentation" title=" color segmentation"> color segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=HSV%20color%20space" title=" HSV color space"> HSV color space</a>, <a href="https://publications.waset.org/abstracts/search?q=immunohistochemistry" title=" immunohistochemistry"> immunohistochemistry</a>, <a href="https://publications.waset.org/abstracts/search?q=meningioma" title=" meningioma"> meningioma</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c-means" title=" fuzzy c-means"> fuzzy c-means</a> </p> <a href="https://publications.waset.org/abstracts/109638/automatic-detection-of-proliferative-cells-in-immunohistochemically-images-of-meningioma-using-fuzzy-c-means-clustering-and-hsv-color-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109638.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">210</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=fuzzy%20k-mode%20clustering&page=2">2</a></li> <li class="page-item"><a class="page-link" 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