LEADER 04421nam 22006855 450 001 9910299849303321 005 20200701121400.0 010 $a3-319-09259-6 024 7 $a10.1007/978-3-319-09259-1 035 $a(CKB)3710000000277607 035 $a(EBL)1965269 035 $a(OCoLC)898086759 035 $a(SSID)ssj0001386262 035 $a(PQKBManifestationID)11752476 035 $a(PQKBTitleCode)TC0001386262 035 $a(PQKBWorkID)11349690 035 $a(PQKB)10401816 035 $a(DE-He213)978-3-319-09259-1 035 $a(MiAaPQ)EBC1965269 035 $a(PPN)183087739 035 $a(EXLCZ)993710000000277607 100 $a20141107d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aPartitional Clustering Algorithms /$fedited by M. Emre Celebi 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (420 p.) 300 $aDescription based upon print version of record. 311 $a3-319-09258-8 320 $aIncludes bibliographical references at the end of each chapters. 327 $aRecent developments in model-based clustering with applications -- Accelerating Lloyd?s algorithm for k-means clustering -- Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm -- Nonsmooth optimization based algorithms in cluster analysis -- Fuzzy Clustering Algorithms and Validity Indices for Distributed Data -- Density Based Clustering: Alternatives to DBSCAN -- Nonnegative matrix factorization for interactive topic modeling and document clustering -- Overview of overlapping partitional clustering methods -- On Semi-Supervised Clustering -- Consensus of Clusterings based on High-order Dissimilarities -- Hubness-Based Clustering of High-Dimensional Data -- Clustering for Monitoring Distributed Data Streams. 330 $aThis book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in realistic applications; Discusses algorithms specifically designed for partitional clustering; Covers center-based, competitive learning, density-based, fuzzy, graph-based, grid-based, metaheuristic, and model-based approaches. 606 $aElectrical engineering 606 $aComputers 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 615 0$aElectrical engineering. 615 0$aComputers. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 14$aCommunications Engineering, Networks. 615 24$aInformation Systems and Communication Service. 615 24$aSignal, Image and Speech Processing. 676 $a005.7 676 $a620 676 $a621.382 702 $aCelebi$b M. Emre$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910299849303321 996 $aPartitional Clustering Algorithms$91412179 997 $aUNINA