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Unsupervised Classification

Similarity Measures, Classical and Metaheuristic Approaches, and Applications

By Sanghamitra Bandyopadhyay , Sriparna Saha

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This book offers a theoretical analysis of symmetry-based clustering techniques. It includes extensive real-world applications in data mining, remote sensing imaging, MR brain imaging, gene expression data analysis, and face detection.

Full Description

  • ISBN13: 978-3-6423-2450-5
  • 280 Pages
  • User Level: Students
  • Publication Date: December 13, 2012
  • Available eBook Formats: PDF
  • eBook Price: $69.95
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Full Description
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.
Table of Contents

Table of Contents

  1. Chap. 1 Introduction.
  2. Chap. 2 Some Single
  3. and Multiobjective Optimization Techniques.
  4. Chap. 3 SimilarityMeasures.
  5. Chap. 4 Clustering Algorithms.
  6. Chap. 5 Point Symmetry Based Distance Measures and their Applications to Clustering.
  7. Chap. 6 A Validity Index Based on Symmetry: Application to Satellite Image Segmentation.
  8. Chap. 7 Symmetry Based Automatic Clustering.
  9. Chap. 8 Some Line Symmetry Distance Based Clustering Techniques.
  10. Chap. 9 Use of Multiobjective Optimization for Data Clustering.
  11. References.
  12. Index.
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