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Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications - Couverture rigide
Synopsis
This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.
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À propos de l?auteur
Olfa Nasraoui is the endowed Chair of e-commerce and the founding director of the Knowledge Discovery & Web Mining Lab at the University of Louisville, where she is also Professor in Computer Engineering & Computer Science. She received her Ph.D in Computer Engineering and Computer Science from the University of Missouri-Columbia in 1999. Her research interests are machine learning algorithms and systems with an emphasis on clustering algorithms, web mining, and recommender systems. She is the recipient of a National Science Foundation CAREER Award and a Best Paper Award for theoretical contributions In computational intelligence at the ANNIE conference.
Chiheb Eddine Ben N'cir received his Ph.D in Computer Science and Management from Higher Institute of Management, University of Tunis, in 2014. Currently, he is an Assistant Professor at the Higher School of Digital Economy (University of Manouba) since 2015 and member of LARODEC laboratory (University of Tunis). He is also a Big Data and Business Intelligence instructor at IBM North Africa and Middle East. His research interests concern unsupervised learning methods and data mining tools with a special emphasis on Big Data clustering, disjoint and non-disjoint partitioning, kernel methods, as well as many other related fields.
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Clustering Methods for Big Data Analytics. Techniques, Toolboxes and Applications.
Edité par
Cham, Springer., 2019
ISBN 10 : 3319978632
ISBN 13 : 9783319978635
Ancien ou d'occasion
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Vendeur : Universitätsbuchhandlung Herta Hold GmbH, Berlin, Allemagne
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IX, 187 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Unsupervised and Semi-Supervised Learning. Sprache: Englisch. N° de réf. du vendeur 9342GB
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Clustering Methods for Big Data Analytics : Techniques, Toolboxes and Applications
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Clustering Methods for Big Data Analytics (Hardcover)
Edité par
Springer International Publishing AG, Cham, 2018
ISBN 10 : 3319978632
ISBN 13 : 9783319978635
Neuf
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Vendeur : Grand Eagle Retail, Mason, OH, Etats-Unis
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Hardcover. Etat : new. Hardcover. This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation. This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783319978635
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Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications (Unsupervised and Semi-Supervised Learning)
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Etat : New. N° de réf. du vendeur ABLIING23Mar3113020113946
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Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications (Unsupervised and Semi-Supervised Learning)
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Etat : New. In. N° de réf. du vendeur ria9783319978635_new
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Clustering Methods for Big Data Analytics
Edité par
Springer International Publishing Nov 2018, 2018
ISBN 10 : 3319978632
ISBN 13 : 9783319978635
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation. 200 pp. Englisch. N° de réf. du vendeur 9783319978635
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Clustering Methods for Big Data Analytics
Edité par
Springer International Publishing, 2018
ISBN 10 : 3319978632
ISBN 13 : 9783319978635
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Includes the most recent and innovative advances in Big Data ClusteringDescribes recent tools, techniques, and frameworks for Big Data AnalyticsIntroduces surveys, applications and case studies of Big Data clustering in Deep L. N° de réf. du vendeur 234946100
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Clustering Methods for Big Data Analytics : Techniques, Toolboxes and Applications
Edité par
Springer, 2018
ISBN 10 : 3319978632
ISBN 13 : 9783319978635
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 33220627
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Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications (Unsupervised and Semi-Supervised Learning)
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Etat : New. N° de réf. du vendeur I-9783319978635
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Clustering Methods for Big Data Analytics
ISBN 10 : 3319978632
ISBN 13 : 9783319978635
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 200 pp. Englisch. N° de réf. du vendeur 9783319978635
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