LEADER 04382nam 22006855 450 001 9910437957903321 005 20200707012427.0 010 $a3-642-32451-7 024 7 $a10.1007/978-3-642-32451-2 035 $a(CKB)2670000000317350 035 $a(EBL)1082549 035 $a(OCoLC)823728319 035 $a(SSID)ssj0000870795 035 $a(PQKBManifestationID)11523251 035 $a(PQKBTitleCode)TC0000870795 035 $a(PQKBWorkID)10819367 035 $a(PQKB)10199497 035 $a(DE-He213)978-3-642-32451-2 035 $a(MiAaPQ)EBC1082549 035 $a(MiAaPQ)EBC6310653 035 $a(PPN)168321858 035 $a(EXLCZ)992670000000317350 100 $a20121212d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aUnsupervised Classification $eSimilarity Measures, Classical and Metaheuristic Approaches, and Applications /$fby Sanghamitra Bandyopadhyay, Sriparna Saha 205 $a1st ed. 2013. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013. 215 $a1 online resource (270 p.) 300 $aDescription based upon print version of record. 311 $a3-642-42836-3 311 $a3-642-32450-9 320 $aIncludes bibliographical references and index. 327 $aChap. 1 Introduction -- Chap. 2 Some Single- and Multiobjective Optimization Techniques -- Chap. 3 SimilarityMeasures -- Chap. 4 Clustering Algorithms -- Chap. 5 Point Symmetry Based Distance Measures and their Applications to Clustering -- Chap. 6 A Validity Index Based on Symmetry: Application to Satellite Image Segmentation -- Chap. 7 Symmetry Based Automatic Clustering -- Chap. 8 Some Line Symmetry Distance Based Clustering Techniques -- Chap. 9 Use of Multiobjective Optimization for Data Clustering -- References -- Index. 330 $aClustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature. This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection. The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging. 606 $aArtificial intelligence 606 $aBioinformatics 606 $aComputers 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 615 0$aArtificial intelligence. 615 0$aBioinformatics. 615 0$aComputers. 615 14$aArtificial Intelligence. 615 24$aComputational Biology/Bioinformatics. 615 24$aInformation Systems and Communication Service. 676 $a001.534 700 $aBandyopadhyay$b Sanghamitra$4aut$4http://id.loc.gov/vocabulary/relators/aut$0471674 702 $aSaha$b Sriparna$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437957903321 996 $aUnsupervised Classification$92523283 997 $aUNINA