Unsupervised Classification

Similarity Measures, Classical and Metaheuristic Approaches, and Applications

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Computer Science, General Computing
Cover of the book Unsupervised Classification by Sanghamitra Bandyopadhyay, Sriparna Saha, Springer Berlin Heidelberg
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Author: Sanghamitra Bandyopadhyay, Sriparna Saha ISBN: 9783642324512
Publisher: Springer Berlin Heidelberg Publication: December 13, 2012
Imprint: Springer Language: English
Author: Sanghamitra Bandyopadhyay, Sriparna Saha
ISBN: 9783642324512
Publisher: Springer Berlin Heidelberg
Publication: December 13, 2012
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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