LEADER 04293nam 22006375 450 001 9910337659403321 005 20200703145436.0 010 $a3-319-97864-0 024 7 $a10.1007/978-3-319-97864-2 035 $a(CKB)4100000007103010 035 $a(DE-He213)978-3-319-97864-2 035 $a(MiAaPQ)EBC5627998 035 $a(PPN)23146357X 035 $a(EXLCZ)994100000007103010 100 $a20181027d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aClustering Methods for Big Data Analytics $eTechniques, Toolboxes and Applications /$fedited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (IX, 187 p. 63 illus., 31 illus. in color.) 225 1 $aUnsupervised and Semi-Supervised Learning,$x2522-848X 311 $a3-319-97863-2 327 $aIntroduction -- 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. 330 $aThis book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation. . 410 0$aUnsupervised and Semi-Supervised Learning,$x2522-848X 606 $aElectrical engineering 606 $aComputational intelligence 606 $aData mining 606 $aBig data 606 $aPattern perception 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aElectrical engineering. 615 0$aComputational intelligence. 615 0$aData mining. 615 0$aBig data. 615 0$aPattern perception. 615 14$aCommunications Engineering, Networks. 615 24$aComputational Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aBig Data/Analytics. 615 24$aPattern Recognition. 676 $a621.382 702 $aNasraoui$b Olfa$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBen N'Cir$b Chiheb-Eddine$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910337659403321 996 $aClustering Methods for Big Data Analytics$92123442 997 $aUNINA