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Partitional Clustering via Nonsmooth Optimization

Clustering via Optimization

  • Adil M. Bagirov
  • Napsu Karmitsa
  • Sona Taheri
Book
  • 537 Downloads

Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Preliminaries

    1. Front Matter
      Pages 1-1
    2. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 3-13
    3. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 15-50
    4. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 51-94
  3. Clustering Algorithms

    1. Front Matter
      Pages 95-95
    2. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 97-133
    3. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 135-163
    4. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 165-183
    5. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 185-200
    6. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 201-223
    7. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 225-241
  4. Implementations and Evaluations of Clustering Algorithms

    1. Front Matter
      Pages 243-243
    2. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 245-268
    3. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 269-279
    4. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 281-314
    5. Adil M. Bagirov, Napsu Karmitsa, Sona Taheri
      Pages 315-317
  5. Back Matter
    Pages 319-336

About this book

Introduction

This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization.

  • Provides a comprehensive description of clustering algorithms based on nonsmooth and global optimization techniques
  • Addresses problems of real-time clustering in large data sets and challenges arising from big data
  • Describes implementation and evaluation of optimization based clustering algorithms

Keywords

Optimization models of clustering problems Clustering with different similarity measures Clustering in very large data sets Real-time clustering algorithms Heuristic clustering algorithms Clustering algorithms based on metaheuristics Nonsmooth optimization based clustering algorithms Global optimization based clustering algorithms Visualization of clustering results Application of clustering algorithms

Authors and affiliations

  • Adil M. Bagirov
    • 1
  • Napsu Karmitsa
    • 2
  • Sona Taheri
    • 3
  1. 1.School of Science, Engineering & Information TechnologyFederation University AustraliaBallaratAustralia
  2. 2.Department of Mathematics and StatisticsUniversity of TurkuTurkuFinland
  3. 3.School of Science, Engineering & Information TechnologyFederation University AustraliaBallaratAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-37826-4
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-37825-7
  • Online ISBN 978-3-030-37826-4
  • Series Print ISSN 2522-848X
  • Series Online ISSN 2522-8498
  • Buy this book on publisher's site
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