LEADER 04806oam 2200637 450 001 9910787573303321 005 20190911103513.0 010 $a1-315-36041-1 010 $a1-4987-8577-8 010 $a1-315-36278-3 010 $a1-315-37351-3 010 $a1-4665-5821-0 035 $a(OCoLC)861794460 035 $a(MiFhGG)GVRL8PZW 035 $a(EXLCZ)992670000000394412 100 $a20130430h20142014 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 00$aData clustering $ealgorithms and applications /$fedited by Charu C. Aggarwal, Chandan K. Reddy 205 $a1st edition 210 1$aBoca Raton :$cCRC Press, Taylor & Francis Group,$d[2014] 210 4$d?2014 215 $a1 online resource (xxvi, 616 pages, 4 unnumbered pages of plates) $cillustrations (some color) 225 0 $aChapman & Hall/CRC data mining and knowledge discovery series 300 $aDescription based upon print version of record. 311 $a1-322-63102-6 311 $a1-4665-5822-9 320 $aIncludes bibliographical references. 327 $aFront Cover; Contents; Preface; Editor Biographies; Contributors; Chapter 1: An Introduction to Cluster Analysis; Chapter 2: Feature Selection for Clustering: A Review; Chapter 3: Probabilistic Models for Clustering; Chapter 4: A Survey of Partitional and Hierarchical Clustering Algorithms; Chapter 5: Density-Based Clustering; Chapter 6: Grid-Based Clustering; Chapter 7: Nonnegative Matrix Factorizations for Clustering: A Survey; Chapter 8: Spectral Clustering; Chapter 9: Clustering High-Dimensional Data; Chapter 10: A Survey of Stream Clustering Algorithms; Chapter 11: Big Data Clustering 327 $aChapter 12: Clustering Categorical DataChapter 13: Document Clustering: The Next Frontier; Chapter 14 : Clustering Multimedia Data; Chapter 15: Time-Series Data Clustering; Chapter 16: Clustering Biological Data; Chapter 17: Network Clustering; Chapter 18: A Survey of Uncertain Data Clustering Algorithms; Chapter 19: Concepts of Visual and Interactive Clustering; Chapter 20: Semisupervised Clustering; Chapter 21: Alternative Clustering Analysis: A Review; Chapter 22 : Cluster Ensembles: Theory and Applications; Chapter 23: Clustering ValidationMeasures 327 $aChapter 24: Educational and Software Resources for DataClusteringColor Inserts; Back Cover 330 3 $aResearch on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization. Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation. In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process?including how to verify the quality of the underlying clusters?through supervision, human intervention, or the automated generation of alternative clusters. 410 0$aChapman & Hall/CRC data mining and knowledge discovery series. 606 $aDocument clustering 606 $aCluster analysis 606 $aData mining 606 $aMachine theory 606 $aFile organization (Computer science) 615 0$aDocument clustering. 615 0$aCluster analysis. 615 0$aData mining. 615 0$aMachine theory. 615 0$aFile organization (Computer science) 676 $a519.535 686 $aBUS061000$aCOM021030$aCOM037000$2bisacsh 702 $aAggarwal$b Charu C. 702 $aReddy$b Chandan K.$f1980- 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910787573303321 996 $aData Clustering$92687086 997 $aUNINA