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Books > Computing & IT > Applications of computing > Databases
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Unsupervised Classification - Similarity Measures, Classical and Metaheuristic Approaches, and Applications (Paperback, 2013 ed.)
Loot Price: R1,741
Discovery Miles 17 410
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Unsupervised Classification - Similarity Measures, Classical and Metaheuristic Approaches, and Applications (Paperback, 2013 ed.)
Expected to ship within 10 - 15 working days
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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.
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