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.
Optimization Methods. Dissimilarity-Based Methods. Methods Based on Probability Models. Methods Based on Density Modes and Level Sets. Specific Cluster and Data Formats. Cluster Validation and Further General Issues.
"The Handbook of Cluster Analysis provides a readable and fairly thorough overview of the highly interdisciplinary and growing field of cluster analysis. The editors rose to the challenge of the Handbook of Modern Statistical Methods series to balance well-developed methods with state-of-the-art research. The book is a collection of papers about how to find groups within data, each written by prominent researchers from computer science, statistics, data science, and elsewhere. Some chapters are application driven while others are solely focused on theory. The editors bookend the text with a solid overview and history of the literature at the beginning, to help newcomers navigate the rest of the handbook, and practical strategies at the end, to help a practitioner choose amongst the competing methods. … Overall, the handbook is a thorough reference for past and present work. It gives the reader a general overview of the field, which is of great value since the work crosses many disciplinary boundaries. The numerous clustering methods are organized to help researchers find the relevant chapters and references therein. …"
— Brianna C. Heggeseth, Williams College, in Journal of the American Statistical Association, July 2017
"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. … This handbook is accessible to readers from various disciplines. …. All articles have a vast amount of hints to literature. So, the greatest benefit is that the interested reader can find the literature for her/his special clustering purpose."
—Rainer Schlittgen, University of Hamburg, Germany, in Statistical Papers, September 2016
"From the wide ranging ‘Handbooks of modern statistical methods’ series, this book seeks to be a non-exhaustive guide to the subject in a large and expanding field. The book is well laid out over