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020 _a9783319978642
_9978-3-319-97864-2
024 7 _a10.1007/978-3-319-97864-2
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
_2thema
082 0 4 _a621.382
_223
245 1 0 _aClustering Methods for Big Data Analytics
_h[electronic resource] :
_bTechniques, Toolboxes and Applications /
_cedited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aIX, 187 p. 63 illus., 31 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUnsupervised and Semi-Supervised Learning,
_x2522-8498
505 0 _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.
520 _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. .
650 0 _aTelecommunication.
_910437
650 0 _aComputational intelligence.
_97716
650 0 _aData mining.
_93907
650 0 _aQuantitative research.
_94633
650 0 _aPattern recognition systems.
_93953
650 1 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aData Mining and Knowledge Discovery.
_943079
650 2 4 _aData Analysis and Big Data.
_943080
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aNasraoui, Olfa.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_943081
700 1 _aBen N'Cir, Chiheb-Eddine.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_943082
710 2 _aSpringerLink (Online service)
_943083
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319978635
776 0 8 _iPrinted edition:
_z9783319978659
776 0 8 _iPrinted edition:
_z9783030074197
830 0 _aUnsupervised and Semi-Supervised Learning,
_x2522-8498
_943084
856 4 0 _uhttps://doi.org/10.1007/978-3-319-97864-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c77248
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