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001 978-3-642-32451-2
003 DE-He213
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008 121212s2013 gw | s |||| 0|eng d
020 _a9783642324512
_9978-3-642-32451-2
024 7 _a10.1007/978-3-642-32451-2
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aBandyopadhyay, Sanghamitra.
_eauthor.
245 1 0 _aUnsupervised Classification
_h[electronic resource] :
_bSimilarity Measures, Classical and Metaheuristic Approaches, and Applications /
_cby Sanghamitra Bandyopadhyay, Sriparna Saha.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXVIII, 262 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aComputer science.
650 0 _aComputers.
650 0 _aArtificial intelligence.
650 0 _aBioinformatics.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputational Biology/Bioinformatics.
650 2 4 _aInformation Systems and Communication Service.
700 1 _aSaha, Sriparna.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642324505
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-32451-2
912 _aZDB-2-SCS
942 _cEBK
999 _c52621
_d52621