Clustering Methods for Big Data Analytics  Techniques, Toolboxes and Applications /  [electronic resource] : 
edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir. 
 - 1st ed. 2019. 
 - IX, 187 p. 63 illus., 31 illus. in color.  online resource. 
 - Unsupervised and Semi-Supervised Learning,  2522-8498 .
 - Unsupervised and Semi-Supervised Learning, .
<br/><br/>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. 
<br/><br/> 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. . 
<br/><br/><label>ISBN: </label>9783319978642 
<br/><br/><label>Standard No.: </label>10.1007/978-3-319-97864-2  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Telecommunication.<br/>Computational intelligence.<br/>Data mining.<br/>Quantitative research.<br/>Pattern recognition systems.<br/>Communications Engineering, Networks.<br/>Computational Intelligence.<br/>Data Mining and Knowledge Discovery.<br/>Data Analysis and Big Data.<br/>Automated Pattern Recognition.
<br/><br/><label>LC Class. No.: </label>TK5101-5105.9 
<br/><br/><label>Dewey Class. No.: </label>621.382