Handbook of cluster analysis

Table of Contents


Cluster Analysis: An Overview
Christian M. Hennig and Marina Meila
A Brief History of Cluster Analysis
Fionn Murtagh

1. Optimization Methods
Quadratic Error and k-Means
Boris Mirkin
K-Medoids and Other Criteria for Crisp Clustering
Douglas Steinley
Foundations for Center-Based Clustering: Worst-Case Approximations and Modern Developments
Pranjal Awasthi and Maria Florina Balcan

2. Dissimilarity-Based Methods
Hierarchical Clustering
Pedro Contreras and Fionn Murtagh
Spectral Clustering
Marina Meila

3. Methods Based on Probability Models
Mixture Models for Standard p-Dimensional Euclidean Data
Geoffrey J. McLachlan and Suren I. Rathnayake
Latent Class Models for Categorical Data
G. Celeux and Gérard Govaert
Dirichlet Process Mixtures and Nonparametric Bayesian Approaches to Clustering
Vinayak Rao
Finite Mixtures of Structured Models
Marco Alfó and Sara Viviani
Time-Series Clustering
Jorge Caiado, Elizabeth Ann Maharaj, and Pierpaolo D’Urso
Clustering Functional Data
David B. Hitchcock and Mark C. Greenwood
Methods Based on Spatial Processes
Lisa Handl, Christian Hirsch, and Volker Schmidt
Significance Testing in Clustering
Hanwen Huang, Yufeng Liu, David Neil Hayes, Andrew Nobel, J.S. Marron, and Christian M. Hennig
Model-Based Clustering for Network Data
Thomas Brendan Murphy

4. Methods Based on Density Modes and Level Sets
A Formulation in Modal Clustering Based on Upper Level Sets
Adelchi Azzalini
Clustering Methods Based on Kernel Density Estimators: Mean-Shift Algorithms
Miguel Á. Carreira-Perpiñán
Nature-Inspired Clustering
Julia Handl and Joshua Knowles

5. Specific Cluster and Data Formats
Semi-Supervised Clustering
Anil Jain, Rong Jin, and Radha Chitta
Clustering of Symbolic Data
Paula Brito
A Survey of Consensus Clustering
Joydeep Ghosh and Ayan Acharya
Two-Mode Partitioning and Multipartitioning
Maurizio Vichi
Fuzzy Clustering
Pierpaolo D’Urso
Rough Set Clustering
Ivo Düntsch and Günther Gediga

6. Cluster Validation and Further General Issues
Method-Independent Indices for Cluster Validation and Estimating the Number of Clusters
Maria Halkidi, Michalis Vazirgiannis, and Christian M. Hennig
Criteria for Comparing Clusterings
Marina Meila
Resampling Methods for Exploring Cluster Stability
Friedrich Leisch
Robustness and Outliers
L.A. García-Escudero, A. Gordaliza, C. Matrán, A. Mayo-Iscar, and Christian M. Hennig
Visual Clustering for Data Analysis and Graphical User Interfaces
Sébastien Déjean and Josiane Mothe
Clustering Strategy and Method Selection
Christian M. Hennig

Features

Gives a broad, in-depth treatment of this dynamic field, covering a wide range of approaches
Discusses topics that play key roles in big data analytics
Presents practical clustering methods that can be implemented directly
Explains the underlying ideas, advantages, and potential limits of the methods
Offers suggestions for software that can be used
Describes new strategies to established approaches
Explores the historical development of cluster analysis, the general strategy of cluster analysis, and relevant issues, such as cluster validation and consensus clustering

Summary

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.


https://www.crcpress.com/Handbook-of-Cluster-Analysis/Hennig-Meila-Murtagh-Rocci/p/book/9781466551886