04293nam 22006375 450 991033765940332120200703145436.03-319-97864-010.1007/978-3-319-97864-2(CKB)4100000007103010(DE-He213)978-3-319-97864-2(MiAaPQ)EBC5627998(PPN)23146357X(EXLCZ)99410000000710301020181027d2019 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierClustering Methods for Big Data Analytics Techniques, Toolboxes and Applications /edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (IX, 187 p. 63 illus., 31 illus. in color.) Unsupervised and Semi-Supervised Learning,2522-848X3-319-97863-2 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.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. .Unsupervised and Semi-Supervised Learning,2522-848XElectrical engineeringComputational intelligenceData miningBig dataPattern perceptionCommunications Engineering, Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/T24035Computational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Data Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Big Data/Analyticshttps://scigraph.springernature.com/ontologies/product-market-codes/522070Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XElectrical engineering.Computational intelligence.Data mining.Big data.Pattern perception.Communications Engineering, Networks.Computational Intelligence.Data Mining and Knowledge Discovery.Big Data/Analytics.Pattern Recognition.621.382Nasraoui Olfaedthttp://id.loc.gov/vocabulary/relators/edtBen N'Cir Chiheb-Eddineedthttp://id.loc.gov/vocabulary/relators/edtBOOK9910337659403321Clustering Methods for Big Data Analytics2123442UNINA