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000825561 019__ $$a1017756014$$a1018393838
000825561 020__ $$a9783319693088$$q(electronic book)
000825561 020__ $$a3319693085$$q(electronic book)
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000825561 020__ $$z9783319693071
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000825561 049__ $$aISEA
000825561 050_4 $$aQA278
000825561 08204 $$a519.5/3$$223
000825561 1001_ $$aWierzchoń, Sławomir T.,$$eauthor.
000825561 24510 $$aModern algorithms of cluster analysis /$$cSławomir T. Wierzchoń, Mieczysław A. Kłopotek.
000825561 260__ $$aCham :$$bSpringer,$$c2018.
000825561 300__ $$a1 online resource.
000825561 336__ $$atext$$btxt$$2rdacontent
000825561 337__ $$acomputer$$bc$$2rdamedia
000825561 338__ $$aonline resource$$bcr$$2rdacarrier
000825561 4901_ $$aStudies in big data ;$$vvolume 34
000825561 504__ $$aIncludes bibliographical references and index.
000825561 506__ $$aAccess limited to authorized users.
000825561 520__ $$aThis book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.
000825561 588__ $$aOnline resource; title from PDF title page (viewed January 10, 2018).
000825561 650_0 $$aCluster analysis.
000825561 650_0 $$aComputer algorithms.
000825561 7001_ $$aKłopotek, Mieczysław A.,$$eauthor.
000825561 77608 $$iPrint version: $$z3319693077$$z9783319693071$$w(OCoLC)1004047934
000825561 830_0 $$aStudies in big data ;$$vv. 34.
000825561 852__ $$bebk
000825561 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-69308-8$$zOnline Access$$91397441.1
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000825561 980__ $$aBIB
000825561 982__ $$aEbook
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