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

chapter 1|20 pages

Cluster Analysis: An Overview

ByChristian Hennig, Marina Meila

chapter 2|10 pages

A Brief History of Cluster Analysis

ByFionn Murtagh

part |2 pages

Section I Optimization Methods

chapter 3|22 pages

Quadratic Error and k-Means

ByBoris Mirkin

chapter 4|12 pages

K-Medoids and Other Criteria for Crisp Clustering

ByDouglas Steinley

chapter 5|34 pages

Foundations for Center-Based Clustering: Worst-Case Approximations and Modern Developments

ByPranjal Awasthi, Maria Florina Balcan

part |2 pages

Section II Dissimilarity-Based Methods

chapter 6|22 pages

Hierarchical Clustering

ByPedro Contreras, Fionn Murtagh

chapter 7|18 pages

Spectral Clustering

ByMarina Meila

part |2 pages

Section III Methods Based on Probability Models

chapter 8|28 pages

Mixture Models for Standard p-Dimensional Euclidean Data

ByGeoffrey J. McLachlan, Suren I. Rathnayake

chapter 9|22 pages

Latent Class Models for Categorical Data

ByG. Celeux, Gérard Govaert

chapter 11|24 pages

Finite Mixtures of Structured Models

ByMarco Alfó, Sara Viviani

chapter 12|24 pages

Time-Series Clustering

ByJorge Caiado, Elizabeth Ann Maharaj, and Pierpaolo D’Urso

chapter 13|24 pages

Clustering Functional Data

ByDavid B. Hitchcock, Mark C. Greenwood

chapter 14|26 pages

Methods Based on Spatial Processes

ByLisa Handl, Christian Hirsch, Volker Schmidt

chapter 15|22 pages

Significance Testing in Clustering

ByHanwen Huang, Yufeng Liu, David Neil Hayes, Andrew Nobel, J.S. Marron, and Christian Hennig

chapter 16|22 pages

Model-Based Clustering for Network Data

ByThomas Brendan Murphy

part |2 pages

Section IV Methods Based on Density Modes and Level Sets

chapter 17|22 pages

A Formulation in Modal Clustering Based on Upper Level Sets

ByAdelchi Azzalini

chapter 18|36 pages

Clustering Methods Based on Kernel Density Estimators: Mean-Shift Algorithms

ByMiguel Á. Carreira-Perpiñán

chapter 19|22 pages

Nature-Inspired Clustering

ByJulia Handl, Joshua Knowles

chapter |2 pages


chapter 20|26 pages

Semi-Supervised Clustering

ByAnil Jain, Rong Jin, Radha Chitta

chapter 21|28 pages

Clustering of Symbolic Data

ByPaula Brito

chapter 22|22 pages

A Survey of Consensus Clustering

ByJoydeep Ghosh, Ayan Acharya

chapter 23|26 pages

Two-Mode Partitioning and Multipartitioning

ByMaurizio Vichi

chapter 24|30 pages

Fuzzy Clustering

ByPierpaolo D’Urso

chapter 25|18 pages

Rough Set Clustering

ByIvo Düntsch, Günther Gediga

part |2 pages

Section VI Cluster Validation and Further General Issues

chapter 26|24 pages

Method-Independent Indices for Cluster Validation and Estimating the Number of Clusters

ByMaria Halkidi, Michalis Vazirgiannis, Christian Hennig

chapter 27|18 pages

Criteria for Comparing Clusterings

ByMarina Meila

chapter 28|16 pages

Resampling Methods for Exploring Cluster Stability

ByFriedrich Leisch

chapter 29|26 pages

Robustness and Outliers

ByL.A. García-Escudero, A. Gordaliza, C. Matrán, A. Mayo-Iscar, and Christian Hennig

chapter 30|24 pages

Visual Clustering for Data Analysis and Graphical User Interfaces

BySébastien Déjean, Josiane Mothe

chapter 31|29 pages

Clustering Strategy and Method Selection

ByChristian Hennig