Overview
- 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
Part of the book series: Unsupervised and Semi-Supervised Learning (UNSESUL)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Editors and Affiliations
About the editors
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.
Bibliographic Information
Book Title: Clustering Methods for Big Data Analytics
Book Subtitle: Techniques, Toolboxes and Applications
Editors: Olfa Nasraoui, Chiheb-Eddine Ben N'Cir
Series Title: Unsupervised and Semi-Supervised Learning
DOI: https://doi.org/10.1007/978-3-319-97864-2
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-319-97863-5Published: 08 November 2018
Softcover ISBN: 978-3-030-07419-7Published: 19 January 2019
eBook ISBN: 978-3-319-97864-2Published: 27 October 2018
Series ISSN: 2522-848X
Series E-ISSN: 2522-8498
Edition Number: 1
Number of Pages: IX, 187
Number of Illustrations: 32 b/w illustrations, 31 illustrations in colour
Topics: Communications Engineering, Networks, Computational Intelligence, Data Mining and Knowledge Discovery, Big Data/Analytics, Pattern Recognition