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Handbook of Cluster Analysis

Chapman & Hall/CRC Handbooks of Modern Statistical Methods

Edited by: Hennig Christian , Meila Marina , Murtagh Fionn , Rocci Roberto

Print publication date:  December  2015
Online publication date:  December  2015

Print ISBN: 9781466551886
eBook ISBN: 9781466551893
Adobe ISBN:

10.1201/b19706
 Cite  Marc Record

Book description

Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools.

The book is organized according to the traditional core approaches to cluster analysis, from the origins to recent developments. After an overview of approaches and a quick journey through the history of cluster analysis, the book focuses on the four major approaches to cluster analysis. These approaches include methods for optimizing an objective function that describes how well data is grouped around centroids, dissimilarity-based methods, mixture models and partitioning models, and clustering methods inspired by nonparametric density estimation. The book also describes additional approaches to cluster analysis, including constrained and semi-supervised clustering, and explores other relevant issues, such as evaluating the quality of a cluster.

This handbook is accessible to readers from various disciplines, reflecting the interdisciplinary nature of cluster analysis. For those already experienced with cluster analysis, the book offers a broad and structured overview. For newcomers to the field, it presents an introduction to key issues. For researchers who are temporarily or marginally involved with cluster analysis problems, the book gives enough algorithmic and practical details to facilitate working knowledge of specific clustering areas.

Table of contents

Chapter  1:  Cluster Analysis: An Overview Download PDF
Chapter  2:  A Brief History of Cluster Analysis Download PDF
Chapter  3:  Quadratic Error and k-Means Download PDF
Chapter  4:  K-Medoids and Other Criteria for Crisp Clustering Download PDF
Chapter  5:  Foundations for Center-Based Clustering: Worst-Case Approximations and Modern Developments Download PDF
Chapter  6:  Hierarchical Clustering Download PDF
Chapter  7:  Spectral Clustering Download PDF
Chapter  8:  Mixture Models for Standard p-Dimensional Euclidean Data Download PDF
Chapter  9:  Latent Class Models for Categorical Data Download PDF
Chapter  10:  Dirichlet Process Mixtures and Nonparametric Bayesian Approaches to Clustering Download PDF
Chapter  11:  Finite Mixtures of Structured Models Download PDF
Chapter  12:  Time-Series Clustering Download PDF
Chapter  13:  Clustering Functional Data Download PDF
Chapter  14:  Methods Based on Spatial Processes Download PDF
Chapter  15:  Significance Testing in Clustering Download PDF
Chapter  16:  Model-Based Clustering for Network Data Download PDF
Chapter  17:  A Formulation in Modal Clustering Based on Upper Level Sets Download PDF
Chapter  18:  Clustering Methods Based on Kernel Density Estimators: Mean-Shift Algorithms Download PDF
Chapter  19:  Nature-Inspired Clustering Download PDF
Chapter  20:  Semi-Supervised Clustering Download PDF
Chapter  21:  Clustering of Symbolic Data Download PDF
Chapter  22:  A Survey of Consensus Clustering Download PDF
Chapter  23:  Two-Mode Partitioning and Multipartitioning Download PDF
Chapter  24:  Fuzzy Clustering Download PDF
Chapter  25:  Rough Set Clustering Download PDF
Chapter  26:  Method-Independent Indices for Cluster Validation and Estimating the Number of Clusters Download PDF
Chapter  27:  Criteria for Comparing Clusterings Download PDF
Chapter  28:  Resampling Methods for Exploring Cluster Stability Download PDF
Chapter  29:  Robustness and Outliers Download PDF
Chapter  30:  Visual Clustering for Data Analysis and Graphical User Interfaces Download PDF
Chapter  31:  Clustering Strategy and Method Selection Download PDF
prelims Download PDF
Index Download PDF
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