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Modern Algorithms of Cluster Analysis

  • Slawomir  Wierzchoń
  • Mieczyslaw Kłopotek

Part of the Studies in Big Data book series (SBD, volume 34)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Sławomir T. Wierzchoń, Mieczysław A. Kłopotek
    Pages 1-7
  3. Sławomir T. Wierzchoń, Mieczysław A. Kłopotek
    Pages 9-66
  4. Sławomir T. Wierzchoń, Mieczysław A. Kłopotek
    Pages 67-161
  5. Sławomir T. Wierzchoń, Mieczysław A. Kłopotek
    Pages 163-180
  6. Sławomir T. Wierzchoń, Mieczysław A. Kłopotek
    Pages 181-259
  7. Sławomir T. Wierzchoń, Mieczysław A. Kłopotek
    Pages 261-314
  8. Sławomir T. Wierzchoń, Mieczysław A. Kłopotek
    Pages 315-317
  9. Back Matter
    Pages 319-421

About this book

Introduction

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.


Keywords

Cluster Analysis Big Data Data Sets Spectral Clustering Combinatorial Cluster Analysis

Authors and affiliations

  • Slawomir  Wierzchoń
    • 1
  • Mieczyslaw Kłopotek
    • 2
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-69308-8
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-69307-1
  • Online ISBN 978-3-319-69308-8
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site