Handbook of Cluster Analysis | Christian Hennig, Marina Meila, Fionn M

Book

Handbook of Cluster Analysis

ABSTRACT

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

TABLE OF CONTENTS

chapter 1|20 pages

Cluster Analysis: An Overview

chapter 2|10 pages

A Brief History of Cluster Analysis

part |2 pages

Section I Optimization Methods

chapter 3|22 pages

Quadratic Error and k-Means

chapter 4|12 pages

K-Medoids and Other Criteria for Crisp Clustering

chapter 5|34 pages

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

part |2 pages

Section II Dissimilarity-Based Methods

chapter 6|22 pages

Hierarchical Clustering

chapter 7|18 pages

Spectral Clustering

part |2 pages

Section III Methods Based on Probability Models

chapter 8|28 pages

Mixture Models for Standard p-Dimensional Euclidean Data

chapter 9|22 pages

Latent Class Models for Categorical Data

chapter 10|22 pages

Dirichlet Process Mixtures and Nonparametric Bayesian Approaches to Clustering

chapter 11|24 pages

Finite Mixtures of Structured Models

chapter 12|24 pages

Time-Series Clustering

chapter 13|24 pages

Clustering Functional Data

chapter 14|26 pages

Methods Based on Spatial Processes

chapter 15|22 pages

Significance Testing in Clustering

chapter 16|22 pages

Model-Based Clustering for Network Data

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

chapter 18|36 pages

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

chapter 19|22 pages

Nature-Inspired Clustering

chapter |2 pages

SectionV

chapter 20|26 pages

Semi-Supervised Clustering

chapter 21|28 pages

Clustering of Symbolic Data

chapter 22|22 pages

A Survey of Consensus Clustering

chapter 23|26 pages

Two-Mode Partitioning and Multipartitioning

chapter 24|30 pages

Fuzzy Clustering

chapter 25|18 pages

Rough Set Clustering

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

chapter 27|18 pages

Criteria for Comparing Clusterings

chapter 28|16 pages

Resampling Methods for Exploring Cluster Stability

chapter 29|26 pages

Robustness and Outliers

chapter 30|24 pages

Visual Clustering for Data Analysis and Graphical User Interfaces

chapter 31|29 pages

Clustering Strategy and Method Selection