Unsupervised Classification : Similarity Measures, Classical and Metaheuristic Approaches, and Applications / by Sanghamitra Bandyopadhyay, Sriparna Saha.

Format:

Book

Author/Creator:

Bandyopadhyay, Sanghamitra, 1968- author.

Saha, Sriparna, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Bioinformatics.

Computers.

Artificial Intelligence.

Computational Biology/Bioinformatics.

Information Systems and Communication Service.

Local Subjects:

Artificial Intelligence.

Computational Biology/Bioinformatics.

Information Systems and Communication Service.

Physical Description:

1 online resource (XVIII, 262 pages)

Edition:

First edition 2013.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-642-32451-2

9783642324512

Access Restriction:

Restricted for use by site license.