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Studies in Big Data

Modern Algorithms of Cluster Analysis

Authors: Wierzchon, Slawomir, Klopotek, Mieczyslaw

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  • Provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, and cluster analysis
  •  Presents a number of approaches to handling a large number of objects within a reasonable time
  •  Presents recent research on cluster analysis
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eBook 96,29 €
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  • ISBN 978-3-319-69308-8
  • Digitally watermarked, DRM-free
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  • Immediate eBook download after purchase
Hardcover 166,39 €
price for Spain (gross)
  • ISBN 978-3-319-69307-1
  • Free shipping for individuals worldwide. COVID-19 shipping restrictions apply.
  • Immediate ebook access, if available*, with your print order
  • Usually ready to be dispatched within 3 to 5 business days.
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Softcover 119,59 €
price for Spain (gross)
  • ISBN 978-3-319-88752-4
  • Free shipping for individuals worldwide. COVID-19 shipping restrictions apply.
  • Immediate ebook access, if available*, with your print order
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About this book

This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc.

 

The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem.

 

Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented.

 

In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.


Table of contents (7 chapters)

Table of contents (7 chapters)
  • Introduction

    Pages 1-7

    Wierzchoń, Sławomir T. (et al.)

  • Cluster Analysis

    Pages 9-66

    Wierzchoń, Sławomir T. (et al.)

  • Algorithms of Combinatorial Cluster Analysis

    Pages 67-161

    Wierzchoń, Sławomir T. (et al.)

  • Cluster Quality Versus Choice of Parameters

    Pages 163-180

    Wierzchoń, Sławomir T. (et al.)

  • Spectral Clustering

    Pages 181-259

    Wierzchoń, Sławomir T. (et al.)

Buy this book

eBook 96,29 €
price for Spain (gross)
  • ISBN 978-3-319-69308-8
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 166,39 €
price for Spain (gross)
  • ISBN 978-3-319-69307-1
  • Free shipping for individuals worldwide. COVID-19 shipping restrictions apply.
  • Immediate ebook access, if available*, with your print order
  • Usually ready to be dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 119,59 €
price for Spain (gross)
  • ISBN 978-3-319-88752-4
  • Free shipping for individuals worldwide. COVID-19 shipping restrictions apply.
  • Immediate ebook access, if available*, with your print order
  • Usually ready to be dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Modern Algorithms of Cluster Analysis
Authors
Series Title
Studies in Big Data
Series Volume
34
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-69308-8
DOI
10.1007/978-3-319-69308-8
Hardcover ISBN
978-3-319-69307-1
Softcover ISBN
978-3-319-88752-4
Series ISSN
2197-6503
Edition Number
1
Number of Pages
XX, 421
Number of Illustrations
51 b/w illustrations
Topics

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