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010   $a3-319-69308-5
024 7 $a10.1007/978-3-319-69308-8
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035   $a(DE-He213)978-3-319-69308-8
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100   $a20171229d2018      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aModern Algorithms of Cluster Analysis /$fby Slawomir Wierzcho?, Mieczyslaw K?opotek
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (XX, 421 p. 51 illus.)
225 1 $aStudies in Big Data,$x2197-6503 ;$v34
311   $a3-319-69307-7 
320   $aIncludes bibliographical references and index.
330   $aThis book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc.   The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem.   Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented.   In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.
410  0$aStudies in Big Data,$x2197-6503 ;$v34
606   $aComputational intelligence
606   $aBig data
606   $aApplied mathematics
606   $aEngineering mathematics
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aApplications of Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/M13003
606   $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070
615  0$aComputational intelligence.
615  0$aBig data.
615  0$aApplied mathematics.
615  0$aEngineering mathematics.
615 14$aComputational Intelligence.
615 24$aBig Data.
615 24$aApplications of Mathematics.
615 24$aBig Data/Analytics.
676   $a519.53
700   $aWierzcho?$b Slawomir$4aut$4http://id.loc.gov/vocabulary/relators/aut$01064404
702   $aK?opotek$b Mieczyslaw$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910299892503321
996   $aModern Algorithms of Cluster Analysis$92537868
997   $aUNINA