04382nam 22006855 450 991043795790332120200707012427.03-642-32451-710.1007/978-3-642-32451-2(CKB)2670000000317350(EBL)1082549(OCoLC)823728319(SSID)ssj0000870795(PQKBManifestationID)11523251(PQKBTitleCode)TC0000870795(PQKBWorkID)10819367(PQKB)10199497(DE-He213)978-3-642-32451-2(MiAaPQ)EBC1082549(MiAaPQ)EBC6310653(PPN)168321858(EXLCZ)99267000000031735020121212d2013 u| 0engur|n|---|||||txtccrUnsupervised Classification Similarity Measures, Classical and Metaheuristic Approaches, and Applications /by Sanghamitra Bandyopadhyay, Sriparna Saha1st ed. 2013.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2013.1 online resource (270 p.)Description based upon print version of record.3-642-42836-3 3-642-32450-9 Includes bibliographical references and index.Chap. 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.Clustering 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.Artificial intelligenceBioinformaticsComputersArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computational Biology/Bioinformaticshttps://scigraph.springernature.com/ontologies/product-market-codes/I23050Information Systems and Communication Servicehttps://scigraph.springernature.com/ontologies/product-market-codes/I18008Artificial intelligence.Bioinformatics.Computers.Artificial Intelligence.Computational Biology/Bioinformatics.Information Systems and Communication Service.001.534Bandyopadhyay Sanghamitraauthttp://id.loc.gov/vocabulary/relators/aut471674Saha Sriparnaauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910437957903321Unsupervised Classification2523283UNINA