Description: Clustering Methods for Big Data Analytics by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description 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. Back Cover 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. Author Biography 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 Ncir 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. Table of Contents Introduction.- Clustering large scale data.- Clustering heterogeneous data.- Distributed clustering methods.- Clustering structured and unstructured data.- Clustering and unsupervised learning for deep learning.- Deep learning methods for clustering.- Clustering high speed cloud, grid, and streaming data.- Extension of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering methods for big data analysis.- Large documents and textual data clustering.- Applications of big data clustering methods.- Clustering multimedia and multi-structured data.- Large-scale recommendation systems and social media systems.- Clustering multimedia and multi-structured data.- Real life applications of big data clustering.- Validation measures for big data clustering methods.- Conclusion. Long Description 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. Feature Includes the most recent and innovative advances in Big Data Clustering Describes recent tools, techniques, and frameworks for Big Data Analytics Introduces surveys, applications and case studies of Big Data clustering in Deep Learning, Blockchain, Cybersecurity, Data Streams, and Tensor graphs Details ISBN3030074196 Language English Year 2019 ISBN-10 3030074196 ISBN-13 9783030074197 Format Paperback Series Unsupervised and Semi-Supervised Learning Subtitle Techniques, Toolboxes and Applications Edited by Chiheb-Eddine Ben NCir DEWEY 006.3 Imprint Springer Nature Switzerland AG Place of Publication Cham Country of Publication Switzerland Pages 187 Publication Date 2019-01-19 Short Title Clustering Methods for Big Data Analytics UK Release Date 2019-01-19 Author Chiheb-Eddine Ben NCir Publisher Springer Nature Switzerland AG Edition Description Softcover reprint of the original 1st ed. 2019 Alternative 9783319978635 Audience Professional & Vocational Illustrations 31 Illustrations, color; 32 Illustrations, black and white; IX, 187 p. 63 illus., 31 illus. in color. 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ISBN-13: 9783030074197
Book Title: Clustering Methods for Big Data Analytics
Number of Pages: 187 Pages
Language: English
Publication Name: Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications
Publisher: Springer Nature Switzerland Ag
Publication Year: 2019
Subject: Engineering & Technology, Computer Science, Business
Item Height: 235 mm
Item Weight: 314 g
Type: Textbook
Author: Chiheb-Eddine Ben N'cir, Olfa Nasraoui
Item Width: 155 mm
Format: Paperback
