Handbook of Cluster Analysis - Hennig, Christian; Meila, Marina; Murtagh, Fionn; Rocci, Roberto (Boca Raton, FL : CRC Press, 2015)
"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. "--Provided by publisher.
Cluster Analysis: An Overview -- Christian M. Hennig and Marina Meila -- A Brief History of Cluster Analysis -- Fionn Murtagh -- 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 -- Dissimilarity-Based Methods -- Hierarchical Clustering -- Pedro Contreras and Fionn Murtagh -- Spectral Clustering -- Marina Meila -- 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 -- 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 -- 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 -
Keywords: Cluster analysis; Spatial analysis; MATHEMATICS -- Applied; MATHEMATICS -- Probability & Statistics -- General; Machine Learning; Statistical Computing; Statistical Theory & Methods