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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="time series data mining"> <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> 38973</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: time series data mining</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">38973</span> Distributed Perceptually Important Point Identification for Time Series Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tak-Chung%20Fu">Tak-Chung Fu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying-Kit%20Hung"> Ying-Kit Hung</a>, <a href="https://publications.waset.org/abstracts/search?q=Fu-Lai%20Chung"> Fu-Lai Chung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is first introduced in 2001. This process originally works for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful cases, it is worth to further explore the opportunity to apply PIP in time series ‘Big Data’. However, the performance of PIP identification is always considered as the limitation when dealing with ‘Big’ time series data. In this paper, two distributed versions of PIP identification based on the Specialized Binary (SB) Tree are proposed. The proposed approaches solve the bottleneck when running the PIP identification process in a standalone computer. Improvement in term of speed is obtained by the distributed versions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20computing" title="distributed computing">distributed computing</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20analysis" title=" performance analysis"> performance analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Perceptually%20Important%20Point%20identification" title=" Perceptually Important Point identification"> Perceptually Important Point identification</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20data%20mining" title=" time series data mining"> time series data mining</a> </p> <a href="https://publications.waset.org/abstracts/84358/distributed-perceptually-important-point-identification-for-time-series-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84358.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">433</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">38972</span> Hierarchical Piecewise Linear Representation of Time Series Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vineetha%20Bettaiah">Vineetha Bettaiah</a>, <a href="https://publications.waset.org/abstracts/search?q=Heggere%20S.%20Ranganath"> Heggere S. Ranganath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the time-amplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA representation achieves dimensionality reduction while preserving prominent local features and general shape of time series. The representation permits course-fine processing at different levels of details, allows flexible definition of similarity based on mathematical measures or general time series shape, and supports time series data mining operations including query by content, clustering and classification based on whole or subsequence similarity. <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=dimensionality%20reduction" title=" dimensionality reduction"> dimensionality reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=piecewise%20linear%20representation" title=" piecewise linear representation"> piecewise linear representation</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20representation" title=" time series representation"> time series representation</a> </p> <a href="https://publications.waset.org/abstracts/2680/hierarchical-piecewise-linear-representation-of-time-series-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2680.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">275</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">38971</span> Block Mining: Block Chain Enabled Process Mining Database</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=James%20Newman">James Newman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process mining is an emerging technology that looks to serialize enterprise data in time series data. It has been used by many companies and has been the subject of a variety of research papers. However, the majority of current efforts have looked at how to best create process mining from standard relational databases. This paper is the first pass at outlining a database custom-built for the minimal viable product of process mining. We present Block Miner, a blockchain protocol to store process mining data across a distributed network. We demonstrate the feasibility of storing process mining data on the blockchain. We present a proof of concept and show how the intersection of these two technologies helps to solve a variety of issues, including but not limited to ransomware attacks, tax documentation, and conflict resolution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blockchain" title="blockchain">blockchain</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20mining" title=" process mining"> process mining</a>, <a href="https://publications.waset.org/abstracts/search?q=memory%20optimization" title=" memory optimization"> memory optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=protocol" title=" protocol"> protocol</a> </p> <a href="https://publications.waset.org/abstracts/166846/block-mining-block-chain-enabled-process-mining-database" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166846.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">102</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">38970</span> A Comprehensive Survey and Improvement to Existing Privacy Preserving Data Mining Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tosin%20Ige">Tosin Ige</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ethics must be a condition of the world, like logic. (Ludwig Wittgenstein, 1889-1951). As important as data mining is, it possess a significant threat to ethics, privacy, and legality, since data mining makes it difficult for an individual or consumer (in the case of a company) to control the accessibility and usage of his data. This research focuses on Current issues and the latest research and development on Privacy preserving data mining methods as at year 2022. It also discusses some advances in those techniques while at the same time highlighting and providing a new technique as a solution to an existing technique of privacy preserving data mining methods. This paper also bridges the wide gap between Data mining and the Web Application Programing Interface (web API), where research is urgently needed for an added layer of security in data mining while at the same time introducing a seamless and more efficient way of data mining. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data" title="data">data</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy" title=" privacy"> privacy</a>, <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=association%20rule" title=" association rule"> association rule</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20preserving" title=" privacy preserving"> privacy preserving</a>, <a href="https://publications.waset.org/abstracts/search?q=mining%20technique" title=" mining technique"> mining technique</a> </p> <a href="https://publications.waset.org/abstracts/145870/a-comprehensive-survey-and-improvement-to-existing-privacy-preserving-data-mining-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145870.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">172</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">38969</span> Representation Data without Lost Compression Properties in Time Series: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nabilah%20Filzah%20Mohd%20Radzuan">Nabilah Filzah Mohd Radzuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Zalinda%20Othman"> Zalinda Othman</a>, <a href="https://publications.waset.org/abstracts/search?q=Azuraliza%20Abu%20Bakar"> Azuraliza Abu Bakar</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Razak%20Hamdan"> Abdul Razak Hamdan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compression%20properties" title="compression properties">compression properties</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertain%20time%20series" title=" uncertain time series"> uncertain time series</a>, <a href="https://publications.waset.org/abstracts/search?q=mining%20technique" title=" mining technique"> mining technique</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20prediction" title=" weather prediction"> weather prediction</a> </p> <a href="https://publications.waset.org/abstracts/1419/representation-data-without-lost-compression-properties-in-time-series-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1419.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">428</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">38968</span> Mining Big Data in Telecommunications Industry: Challenges, Techniques, and Revenue Opportunity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hoda%20A.%20Abdel%20Hafez">Hoda A. Abdel Hafez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mining%20big%20data" title="mining big data">mining big data</a>, <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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=telecommunication" title=" telecommunication"> telecommunication</a> </p> <a href="https://publications.waset.org/abstracts/41412/mining-big-data-in-telecommunications-industry-challenges-techniques-and-revenue-opportunity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41412.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">409</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">38967</span> A Review Paper on Data Mining and Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sikander%20Singh%20Cheema">Sikander Singh Cheema</a>, <a href="https://publications.waset.org/abstracts/search?q=Jasmeen%20Kaur"> Jasmeen Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the concept of data mining is summarized and its one of the important process i.e KDD is summarized. The data mining based on Genetic Algorithm is researched in and ways to achieve the data mining Genetic Algorithm are surveyed. This paper also conducts a formal review on the area of data mining tasks and genetic algorithm in various fields. <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=KDD" title=" KDD"> KDD</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=descriptive%20mining" title=" descriptive mining"> descriptive mining</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20mining" title=" predictive mining"> predictive mining</a> </p> <a href="https://publications.waset.org/abstracts/43637/a-review-paper-on-data-mining-and-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43637.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">591</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">38966</span> Multi-scale Spatial and Unified Temporal Feature-fusion Network for Multivariate Time Series Anomaly Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hang%20Yang">Hang Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jichao%20Li"> Jichao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Kewei%20Yang"> Kewei Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tianyang%20Lei"> Tianyang Lei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis. <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=industrial%20system" title=" industrial system"> industrial system</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series" title=" multivariate time series"> multivariate time series</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a> </p> <a href="https://publications.waset.org/abstracts/193205/multi-scale-spatial-and-unified-temporal-feature-fusion-network-for-multivariate-time-series-anomaly-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193205.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">14</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">38965</span> Investigation on Performance of Change Point Algorithm in Time Series Dynamical Regimes and Effect of Data Characteristics </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farhad%20Asadi">Farhad Asadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Javad%20Mollakazemi"> Mohammad Javad Mollakazemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reﬂected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series" title="time series">time series</a>, <a href="https://publications.waset.org/abstracts/search?q=fluctuation%20in%20statistical%20characteristics" title=" fluctuation in statistical characteristics"> fluctuation in statistical characteristics</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20learning" title=" optimal learning"> optimal learning</a>, <a href="https://publications.waset.org/abstracts/search?q=change-point%20algorithm" title=" change-point algorithm"> change-point algorithm</a> </p> <a href="https://publications.waset.org/abstracts/18167/investigation-on-performance-of-change-point-algorithm-in-time-series-dynamical-regimes-and-effect-of-data-characteristics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18167.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">426</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">38964</span> Time Series Regression with Meta-Clusters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Monika%20Chuchro">Monika Chuchro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique. <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=data%20analysis" title=" data analysis"> data analysis</a>, <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=predictive%20models" title=" predictive models"> predictive models</a> </p> <a href="https://publications.waset.org/abstracts/3788/time-series-regression-with-meta-clusters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3788.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">466</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">38963</span> Quantum Statistical Machine Learning and Quantum Time Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Alzeley">Omar Alzeley</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Utev"> Sergey Utev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation%20techniques" title=" simulation techniques"> simulation techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20probability" title=" quantum probability"> quantum probability</a>, <a href="https://publications.waset.org/abstracts/search?q=tensor%20product" title=" tensor product"> tensor product</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a> </p> <a href="https://publications.waset.org/abstracts/52720/quantum-statistical-machine-learning-and-quantum-time-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52720.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">469</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">38962</span> A Modular Framework for Enabling Analysis for Educators with Different Levels of Data Mining Skills</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kyle%20De%20Freitas">Kyle De Freitas</a>, <a href="https://publications.waset.org/abstracts/search?q=Margaret%20Bernard"> Margaret Bernard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Enabling data mining analysis among a wider audience of educators is an active area of research within the educational data mining (EDM) community. The paper proposes a framework for developing an environment that caters for educators who have little technical data mining skills as well as for more advanced users with some data mining expertise. This framework architecture was developed through the review of the strengths and weaknesses of existing models in the literature. The proposed framework provides a modular architecture for future researchers to focus on the development of specific areas within the EDM process. Finally, the paper also highlights a strategy of enabling analysis through either the use of predefined questions or a guided data mining process and highlights how the developed questions and analysis conducted can be reused and extended over time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=educational%20data%20mining" title="educational data mining">educational data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20management%20system" title=" learning management system"> learning management system</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=EDM%20framework" title=" EDM framework"> EDM framework</a> </p> <a href="https://publications.waset.org/abstracts/78786/a-modular-framework-for-enabling-analysis-for-educators-with-different-levels-of-data-mining-skills" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78786.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">326</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">38961</span> Association Rules Mining and NOSQL Oriented Document in Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sarra%20Senhadji">Sarra Senhadji</a>, <a href="https://publications.waset.org/abstracts/search?q=Imene%20Benzeguimi"> Imene Benzeguimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Zohra%20Yagoub"> Zohra Yagoub</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Apriori" title="Apriori">Apriori</a>, <a href="https://publications.waset.org/abstracts/search?q=Association%20rules%20mining" title=" Association rules mining"> Association rules mining</a>, <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=Data%20Mining" title=" Data Mining"> Data Mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop"> Hadoop</a>, <a href="https://publications.waset.org/abstracts/search?q=MapReduce" title=" MapReduce"> MapReduce</a>, <a href="https://publications.waset.org/abstracts/search?q=MongoDB" title=" MongoDB"> MongoDB</a>, <a href="https://publications.waset.org/abstracts/search?q=NoSQL" title=" NoSQL"> NoSQL</a> </p> <a href="https://publications.waset.org/abstracts/126206/association-rules-mining-and-nosql-oriented-document-in-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126206.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">160</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">38960</span> Frequent Item Set Mining for Big Data Using MapReduce Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tamanna%20Jethava">Tamanna Jethava</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahul%20Joshi"> Rahul Joshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequent%20item%20set%20mining" title="frequent item set mining">frequent item set mining</a>, <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=Hadoop" title=" Hadoop"> Hadoop</a>, <a href="https://publications.waset.org/abstracts/search?q=MapReduce" title=" MapReduce"> MapReduce</a> </p> <a href="https://publications.waset.org/abstracts/49592/frequent-item-set-mining-for-big-data-using-mapreduce-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49592.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">435</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">38959</span> Indexing and Incremental Approach Using Map Reduce Bipartite Graph (MRBG) for Mining Evolving Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adarsh%20Shroff">Adarsh Shroff</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining. <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=map%20reduce" title=" map reduce"> map reduce</a>, <a href="https://publications.waset.org/abstracts/search?q=incremental%20processing" title=" incremental processing"> incremental processing</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20computation" title=" iterative computation"> iterative computation</a> </p> <a href="https://publications.waset.org/abstracts/46413/indexing-and-incremental-approach-using-map-reduce-bipartite-graph-mrbg-for-mining-evolving-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46413.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">350</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">38958</span> Data-Mining Approach to Analyzing Industrial Process Information for Real-Time Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seung-Lock%20Seo">Seung-Lock Seo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults. <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=process%20data" title=" process data"> process data</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=safety" title=" safety"> safety</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20processes" title=" industrial processes"> industrial processes</a> </p> <a href="https://publications.waset.org/abstracts/3929/data-mining-approach-to-analyzing-industrial-process-information-for-real-time-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3929.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">400</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">38957</span> Algorithms used in Spatial Data Mining GIS</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Bairami%20Rad">Vahid Bairami Rad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extracting knowledge from spatial data like GIS data is important to reduce the data and extract information. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. We introduced a database-oriented framework for spatial data mining which is based on the concepts of neighborhood graphs and paths. A small set of basic operations on these graphs and paths were defined as database primitives for spatial data mining. Furthermore, techniques to efficiently support the database primitives by a commercial DBMS were presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20data%20base" title="spatial data base">spatial data base</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20database" title=" knowledge discovery database"> knowledge discovery database</a>, <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=spatial%20relationship" title=" spatial relationship"> spatial relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20data%20mining" title=" predictive data mining"> predictive data mining</a> </p> <a href="https://publications.waset.org/abstracts/29004/algorithms-used-in-spatial-data-mining-gis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29004.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">460</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">38956</span> Applying a Noise Reduction Method to Reveal Chaos in the River Flow Time Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20H.%20Fattahi">Mohammad H. Fattahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. The noise level of time series was estimated with the aid of Gaussian kernel algorithm. This step was found to be crucial in preventing removal of the vital data such as memory, correlation and trend from the time series in addition to the noise during de-noising process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chaotic%20behavior" title="chaotic behavior">chaotic behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20reduction" title=" noise reduction"> noise reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=river%20flow" title=" river flow"> river flow</a> </p> <a href="https://publications.waset.org/abstracts/12972/applying-a-noise-reduction-method-to-reveal-chaos-in-the-river-flow-time-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12972.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">468</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">38955</span> Frequent Itemset Mining Using Rough-Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Usman%20Qamar">Usman Qamar</a>, <a href="https://publications.waset.org/abstracts/search?q=Younus%20Javed"> Younus Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rough-sets" title="rough-sets">rough-sets</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=outliers" title=" outliers"> outliers</a>, <a href="https://publications.waset.org/abstracts/search?q=frequent%20itemset%20mining" title=" frequent itemset mining"> frequent itemset mining</a> </p> <a href="https://publications.waset.org/abstracts/14372/frequent-itemset-mining-using-rough-sets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14372.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">437</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">38954</span> Reviewing Privacy Preserving Distributed Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sajjad%20Baghernezhad">Sajjad Baghernezhad</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeideh%20Baghernezhad"> Saeideh Baghernezhad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner’s privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined. <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=distributed%20data%20mining" title=" distributed data mining"> distributed data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20protection" title=" privacy protection"> privacy protection</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20preserving" title=" privacy preserving"> privacy preserving</a> </p> <a href="https://publications.waset.org/abstracts/28876/reviewing-privacy-preserving-distributed-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28876.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">525</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">38953</span> Knowledge Discovery and Data Mining Techniques in Textile Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Filiz%20Ersoz">Filiz Ersoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Taner%20Ersoz"> Taner Ersoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Erkin%20Guler"> Erkin Guler</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship. <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=textile%20production" title=" textile production"> textile production</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20trees" title=" decision trees"> decision trees</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/75461/knowledge-discovery-and-data-mining-techniques-in-textile-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75461.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">349</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">38952</span> Harmonic Data Preparation for Clustering and Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Asheibi">Ali Asheibi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Preparing raw data to be ready for data mining exploration take up most of the effort and time spent in the whole data mining process. Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. Large amounts of harmonic data have been collected from an actual harmonic monitoring system in a distribution system in Australia for three years. This amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. In this paper, harmonic data preparation processes to better understanding of the data have been presented. Underlying classes in this data has then been identified using clustering technique based on the Minimum Message Length (MML) method. The underlying operational information contained within the clusters can be rapidly visualised by the engineers. The C5.0 algorithm was used for classification and interpretation of the generated clusters. <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=harmonic%20data" title=" harmonic data"> harmonic data</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/80801/harmonic-data-preparation-for-clustering-and-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80801.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">247</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">38951</span> Classification of Generative Adversarial Network Generated Multivariate Time Series Data Featuring Transformer-Based Deep Learning Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thrivikraman%20Aswathi">Thrivikraman Aswathi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Advaith"> S. Advaith</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GAN" title="GAN">GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series" title=" multivariate time series"> multivariate time series</a> </p> <a href="https://publications.waset.org/abstracts/157460/classification-of-generative-adversarial-network-generated-multivariate-time-series-data-featuring-transformer-based-deep-learning-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157460.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">130</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">38950</span> Efficient Frequent Itemset Mining Methods over Real-Time Spatial Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamdi%20Sana">Hamdi Sana</a>, <a href="https://publications.waset.org/abstracts/search?q=Emna%20Bouazizi"> Emna Bouazizi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sami%20Faiz"> Sami Faiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, there is a huge increase in the use of spatio-temporal applications where data and queries are continuously moving. As a result, the need to process real-time spatio-temporal data seems clear and real-time stream data management becomes a hot topic. Sliding window model and frequent itemset mining over dynamic data are the most important problems in the context of data mining. Thus, sliding window model for frequent itemset mining is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. These methods use the traditional transaction-based sliding window model where the window size is based on a fixed number of transactions. Actually, this model supposes that all transactions have a constant rate which is not suited for real-time applications. And the use of this model in such applications endangers their performance. Based on these observations, this paper relaxes the notion of window size and proposes the use of a timestamp-based sliding window model. In our proposed frequent itemset mining algorithm, support conditions are used to differentiate frequents and infrequent patterns. Thereafter, a tree is developed to incrementally maintain the essential information. We evaluate our contribution. The preliminary results are quite promising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=real-time%20spatial%20big%20data" title="real-time spatial big data">real-time spatial big data</a>, <a href="https://publications.waset.org/abstracts/search?q=frequent%20itemset" title=" frequent itemset"> frequent itemset</a>, <a href="https://publications.waset.org/abstracts/search?q=transaction-based%20sliding%20window%20model" title=" transaction-based sliding window model"> transaction-based sliding window model</a>, <a href="https://publications.waset.org/abstracts/search?q=timestamp-based%20sliding%20window%20model" title=" timestamp-based sliding window model"> timestamp-based sliding window model</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20frequent%20patterns" title=" weighted frequent patterns"> weighted frequent patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=tree" title=" tree"> tree</a>, <a href="https://publications.waset.org/abstracts/search?q=stream%20query" title=" stream query"> stream query</a> </p> <a href="https://publications.waset.org/abstracts/102447/efficient-frequent-itemset-mining-methods-over-real-time-spatial-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102447.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">161</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">38949</span> Healthcare Data Mining Innovations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eugenia%20Jilinguirian">Eugenia Jilinguirian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the healthcare industry, data mining is essential since it transforms the field by collecting useful data from large datasets. Data mining is the process of applying advanced analytical methods to large patient records and medical histories in order to identify patterns, correlations, and trends. Healthcare professionals can improve diagnosis accuracy, uncover hidden linkages, and predict disease outcomes by carefully examining these statistics. Additionally, data mining supports personalized medicine by personalizing treatment according to the unique attributes of each patient. This proactive strategy helps allocate resources more efficiently, enhances patient care, and streamlines operations. However, to effectively apply data mining, however, and ensure the use of private healthcare information, issues like data privacy and security must be carefully considered. Data mining continues to be vital for searching for more effective, efficient, and individualized healthcare solutions as technology evolves. <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=healthcare" title=" healthcare"> healthcare</a>, <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=individualised%20healthcare" title=" individualised healthcare"> individualised healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare%20solutions" title=" healthcare solutions"> healthcare solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=database" title=" database"> database</a> </p> <a href="https://publications.waset.org/abstracts/178640/healthcare-data-mining-innovations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178640.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">66</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">38948</span> Data Mining Approach for Commercial Data Classification and Migration in Hybrid Storage Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mais%20Haj%20Qasem">Mais Haj Qasem</a>, <a href="https://publications.waset.org/abstracts/search?q=Maen%20M.%20Al%20Assaf"> Maen M. Al Assaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Rodan"> Ali Rodan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20storage%20system" title="hybrid storage system">hybrid storage system</a>, <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=recurrent%20neural%20network" title=" recurrent neural network"> recurrent neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/43112/data-mining-approach-for-commercial-data-classification-and-migration-in-hybrid-storage-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43112.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">307</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">38947</span> Data Mining Practices: Practical Studies on the Telecommunication Companies in Jordan </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dina%20Ahmad%20Alkhodary">Dina Ahmad Alkhodary</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aimed to investigate the practices of Data Mining on the telecommunication companies in Jordan, from the viewpoint of the respondents. In order to achieve the goal of the study, and test the validity of hypotheses, the researcher has designed a questionnaire to collect data from managers and staff members from main department in the researched companies. The results shows improvements stages of the telecommunications companies towered Data Mining. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data" title="data">data</a>, <a href="https://publications.waset.org/abstracts/search?q=mining" title=" mining"> mining</a>, <a href="https://publications.waset.org/abstracts/search?q=development" title=" development"> development</a>, <a href="https://publications.waset.org/abstracts/search?q=business" title=" business"> business</a> </p> <a href="https://publications.waset.org/abstracts/29559/data-mining-practices-practical-studies-on-the-telecommunication-companies-in-jordan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29559.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">497</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">38946</span> Environmental Impact Assessment in Mining Regions with Remote Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carla%20Palencia-Aguilar">Carla Palencia-Aguilar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Calculations of Net Carbon Balance can be obtained by means of Net Biome Productivity (NBP), Net Ecosystem Productivity (NEP), and Net Primary Production (NPP). The latter is an important component of the biosphere carbon cycle and is easily obtained data from MODIS MOD17A3HGF; however, the results are only available yearly. To overcome data availability, bands 33 to 36 from MODIS MYD021KM (obtained on a daily basis) were analyzed and compared with NPP data from the years 2000 to 2021 in 7 sites where surface mining takes place in the Colombian territory. Coal, Gold, Iron, and Limestone were the minerals of interest. Scales and Units as well as thermal anomalies, were considered for net carbon balance per location. The NPP time series from the satellite images were filtered by using two Matlab filters: First order and Discrete Transfer. After filtering the NPP time series, comparing the graph results from the satellite’s image value, and running a linear regression, the results showed R2 from 0,72 to 0,85. To establish comparable units among NPP and bands 33 to 36, the Greenhouse Gas Equivalencies Calculator by EPA was used. The comparison was established in two ways: one by the sum of all the data per point per year and the other by the average of 46 weeks and finding the percentage that the value represented with respect to NPP. The former underestimated the total CO2 emissions. The results also showed that coal and gold mining in the last 22 years had less CO2 emissions than limestone, with an average per year of 143 kton CO2 eq for gold, 152 kton CO2 eq for coal, and 287 kton CO2 eq for iron. Limestone emissions varied from 206 to 441 kton CO2 eq. The maximum emission values from unfiltered data correspond to 165 kton CO2 eq. for gold, 188 kton CO2 eq. for coal, and 310 kton CO2 eq. for iron and limestone, varying from 231 to 490 kton CO2 eq. If the most pollutant limestone site improves its production technology, limestone could count with a maximum of 318 kton CO2 eq emissions per year, a value very similar respect to iron. The importance of gathering data is to establish benchmarks in order to attain 2050’s zero emissions goal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=carbon%20dioxide" title="carbon dioxide">carbon dioxide</a>, <a href="https://publications.waset.org/abstracts/search?q=NPP" title=" NPP"> NPP</a>, <a href="https://publications.waset.org/abstracts/search?q=MODIS" title=" MODIS"> MODIS</a>, <a href="https://publications.waset.org/abstracts/search?q=MINING" title=" MINING"> MINING</a> </p> <a href="https://publications.waset.org/abstracts/157412/environmental-impact-assessment-in-mining-regions-with-remote-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157412.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">104</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">38945</span> Recent Advances in Data Warehouse</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Hanash%20Alzahrani">Fahad Hanash Alzahrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes some recent advances in a quickly developing area of data storing and processing based on Data Warehouses and Data Mining techniques, which are associated with software, hardware, data mining algorithms and visualisation techniques having common features for any specific problems and tasks of their implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title="data warehouse">data warehouse</a>, <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=knowledge%20discovery%20in%20databases" title=" knowledge discovery in databases"> knowledge discovery in databases</a>, <a href="https://publications.waset.org/abstracts/search?q=on-line%20analytical%20processing" title=" on-line analytical processing"> on-line analytical processing</a> </p> <a href="https://publications.waset.org/abstracts/63299/recent-advances-in-data-warehouse" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63299.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">404</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">38944</span> Analysis of Dynamics Underlying the Observation Time Series by Using a Singular Spectrum Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Delage">O. Delage</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Bencherif"> H. Bencherif</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Portafaix"> T. Portafaix</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Bourdier"> A. Bourdier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of time series analysis is to learn about the dynamics behind some time ordered measurement data. Two approaches are used in the literature to get a better knowledge of the dynamics contained in observation data sequences. The first of these approaches concerns time series decomposition, which is an important analysis step allowing patterns and behaviors to be extracted as components providing insight into the mechanisms producing the time series. As in many cases, time series are short, noisy, and non-stationary. To provide components which are physically meaningful, methods such as Empirical Mode Decomposition (EMD), Empirical Wavelet Transform (EWT) or, more recently, Empirical Adaptive Wavelet Decomposition (EAWD) have been proposed. The second approach is to reconstruct the dynamics underlying the time series as a trajectory in state space by mapping a time series into a set of Rᵐ lag vectors by using the method of delays (MOD). Takens has proved that the trajectory obtained with the MOD technic is equivalent to the trajectory representing the dynamics behind the original time series. This work introduces the singular spectrum decomposition (SSD), which is a new adaptive method for decomposing non-linear and non-stationary time series in narrow-banded components. This method takes its origin from singular spectrum analysis (SSA), a nonparametric spectral estimation method used for the analysis and prediction of time series. As the first step of SSD is to constitute a trajectory matrix by embedding a one-dimensional time series into a set of lagged vectors, SSD can also be seen as a reconstruction method like MOD. We will first give a brief overview of the existing decomposition methods (EMD-EWT-EAWD). The SSD method will then be described in detail and applied to experimental time series of observations resulting from total columns of ozone measurements. The results obtained will be compared with those provided by the previously mentioned decomposition methods. We will also compare the reconstruction qualities of the observed dynamics obtained from the SSD and MOD methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series%20analysis" title="time series analysis">time series analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20time%20series%20decomposition" title=" adaptive time series decomposition"> adaptive time series decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20space%20reconstruction" title=" phase space reconstruction"> phase space reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20spectrum%20analysis" title=" singular spectrum analysis"> singular spectrum analysis</a> </p> <a 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