eBook Data Clustering: Algorithms and Applications, 1st Edition

  • Charu C. Aggarwal
  • Published By: Chapman & Hall
  • ISBN-10: 1466558229
  • ISBN-13: 9781466558229
  • DDC: 519.53
  • Grade Level Range: College Freshman - College Senior
  • 652 Pages | eBook
  • Original Copyright 2013 | Published/Released February 2016
  • This publication's content originally published in print form: 2013
  • Price:  Sign in for price

About

Overview

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.

Table of Contents

Front Cover.
Half Title Page.
Other Frontmatter.
Title Page.
Copyright Page.
Contents.
Preface.
1: Editor Biographies.
Contributors.
2: An Introduction to Cluster Analysis.
3: Feature Selection for Clustering: A Review.
4: Probabilistic Models for Clustering.
5: A Survey of Partitional and Hierarchical Clustering Algorithms.
6: Density-Based Clustering.
7: Grid-Based Clustering.
8: Nonnegative Matrix Factorizations for Clustering: A Survey.
9: Spectral Clustering.
10: Clustering High-Dimensional Data.
11: A Survey of Stream Clustering Algorithms.
12: Big Data Clustering.
13: Clustering Categorical Data.
14: Document Clustering: The Next Frontier.
15: Clustering Multimedia Data.
16: Time-Series Data Clustering.
17: Clustering Biological Data.
18: Network Clustering.
19: A Survey of Uncertain Data Clustering Algorithms.
20: Concepts of Visual and Interactive Clustering.
21: Semisupervised Clustering.
22: Alternative Clustering Analysis: A Review.
23: Cluster Ensembles: Theory and Applications.
24: Clustering Validation Measures.
25: Educational and Software Resources for Data Clustering.
Front Cover.
Half Title Page.
Other Frontmatter.
Title Page.
Copyright Page.
Contents.
Preface.
1: Editor Biographies.
Contributors.
2: An Introduction to Cluster Analysis.
3: Feature Selection for Clustering: A Review.
4: Probabilistic Models for Clustering.
5: A Survey of Partitional and Hierarchical Clustering Algorithms.
6: Density-Based Clustering.
7: Grid-Based Clustering.
8: Nonnegative Matrix Factorizations for Clustering: A Survey.
9: Spectral Clustering.
10: Clustering High-Dimensional Data.
11: A Survey of Stream Clustering Algorithms.
12: Big Data Clustering.
13: Clustering Categorical Data.
14: Document Clustering: The Next Frontier.
15: Clustering Multimedia Data.
16: Time-Series Data Clustering.
17: Clustering Biological Data.
18: Network Clustering.
19: A Survey of Uncertain Data Clustering Algorithms.
20: Concepts of Visual and Interactive Clustering.
21: Semisupervised Clustering.
22: Alternative Clustering Analysis: A Review.
23: Cluster Ensembles: Theory and Applications.
24: Clustering Validation Measures.
25: Educational and Software Resources for Data Clustering.