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TitleClustering Methods for Big Data Analytics [electronic resource] : Techniques, Toolboxes and Applications / edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir
ImprintCham : Springer International Publishing : Imprint: Springer, 2019
Connect tohttps://doi.org/10.1007/978-3-319-97864-2
Descript IX, 187 p. 63 illus., 31 illus. in color. online resource

SUMMARY

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.


CONTENT

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


Telecommunication Engineering Data mining Big data Optical pattern recognition Communications Engineering Networks. http://scigraph.springernature.com/things/product-market-codes/T24035 Computational Intelligence. http://scigraph.springernature.com/things/product-market-codes/T11014 Data Mining and Knowledge Discovery. http://scigraph.springernature.com/things/product-market-codes/I18030 Big Data/Analytics. http://scigraph.springernature.com/things/product-market-codes/522070 Pattern Recognition. http://scigraph.springernature.com/things/product-market-codes/I2203X



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