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E-raamat: Handbook of Cluster Analysis

Edited by (University of Derby and Goldsmiths University of London, UK), Edited by (University of Washington, Seattle, USA), Edited by (University College London, UK), Edited by (University of Rome Tor Vergata, Italy)
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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.

Arvustused

"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

Preface xi
Editors xv
Contributors xvii
1 Cluster Analysis: An Overview
1(20)
Christian Hennig
Marina Meila
2 A Brief History of Cluster Analysis
21(12)
Fionn Murtagh
Section I Optimization Methods
3 Quadratic Error and k-Means
33(22)
Boris Mirkin
4 K-Medoids and Other Criteria for Crisp Clustering
55(12)
Douglas Steinley
5 Foundations for Center-Based Clustering: Worst-Case Approximations and Modern Developments
67(36)
Pranjal Awasthi
Maria Florina Balcan
Section II Dissimilarity-Based Methods
6 Hierarchical Clustering
103(22)
Pedro Contreras
Fionn Murtagh
7 Spectral Clustering
125(20)
Marina Meila
Section III Methods Based on Probability Models
8 Mixture Models for Standard p-Dimensional Euclidean Data
145(28)
Geoffrey J. McLachlan
Suren I. Rathnayake
9 Latent Class Models for Categorical Data
173(22)
G. Celeux
Gerard Govaert
10 Dirichlet Process Mixtures and Nonparametric Bayesian Approaches to Clustering
195(22)
Vinayak Rao
11 Finite Mixtures of Structured Models
217(24)
Marco Alfo
Sara Viviani
12 Time-Series Clustering
241(24)
Jorge Caiado
Elizabeth Ann Maharaj
Pierpaolo D'Urso
13 Clustering Functional Data
265(24)
David B. Hitchcock
Mark C. Greenwood
14 Methods Based on Spatial Processes
289(26)
Lisa Handl
Christian Hirsch
Volker Schmidt
15 Significance Testing in Clustering
315(22)
Hanwen Huang
Yufeng Liu
David Neil Hayes
Andrew Nobel
J.S. Marron
Christian Hennig
16 Model-Based Clustering for Network Data
337(24)
Thomas Brendan Murphy
Section IV Methods Based on Density Modes and Level Sets
17 A Formulation in Modal Clustering Based on Upper Level Sets
361(22)
Adelchi Azzalini
18 Clustering Methods Based on Kernel Density Estimators: Mean-Shift Algorithms
383(36)
Miguel A. Carreira-Perpinan
19 Nature-Inspired Clustering
419(24)
Julia Handl
Joshua Knowles
Section V Specific Cluster and Data Formats
20 Semi-Supervised Clustering
443(26)
Anil Jain
Rong Jin
Radha Chitta
21 Clustering of Symbolic Data
469(28)
Paula Brito
22 A Survey of Consensus Clustering
497(22)
Joydeep Ghosh
Ayan Acharya
23 Two-Mode Partitioning and Multipartitioning
519(26)
Maurizio Vichi
24 Fuzzy Clustering
545(30)
Pierpaolo D'Urso
25 Rough Set Clustering
575(20)
Ivo Duntsch
Gunther Gediga
Section VI Cluster Validation and Further General Issues
26 Method-Independent Indices for Cluster Validation and Estimating the Number of Clusters
595(24)
Maria Halkidi
Michalis Vazirgiannis
Christian Hennig
27 Criteria for Comparing Clusterings
619(18)
Marina Meila
28 Resampling Methods for Exploring Cluster Stability
637(16)
Friedrich Leisch
29 Robustness and Outliers
653(26)
L.A. Garcia-Escudero
A. Gordaliza
C. Matran
A. Mayo-Iscar
Christian Hennig
30 Visual Clustering for Data Analysis and Graphical User Interfaces
679(24)
Sebastien Dejean
Josiane Mothe
31 Clustering Strategy and Method Selection
703(28)
Christian Hennig
Index 731
Christian Hennig is a senior lecturer in the Department of Statistical Science at University College London. Dr. Hennig is currently secretary of the International Federation of Classification Societies and associate editor of Statistics and Computing, Computational Statistics and Data Analysis, Advances in Data Analysis and Classification, and Statistical Methods and Applications. His main research interests are cluster analysis, philosophy of statistics, robust statistics, multivariate analysis, data visualization, and model selection.

Marina Meila is a professor of statistics at the University of Washington. She earned a PhD in computer science and electrical engineering from the Massachusetts Institute of Technology. Her long-term interest is in machine learning and reasoning in uncertainty and how these can be performed efficiently on large, complex data sets.

Fionn Murtagh is a professor of data science at University of Derby and Goldsmiths University of London. Dr. Murtagh is a fellow of the International Association for Pattern Recognition, a fellow of the British Computer Society, an elected member of the Royal Irish Academy and Academia Europaea, a member of the editorial boards of many journals, and editor-in-chief of the Computer Journal. His research interests encompass data science and big data analytics.

Roberto Rocci is a professor of statistics in the Department of Economics and Finance at the University of Rome Tor Vergata. Dr. Rocci is associate editor of the Statistical Methods and Applications Journal and board member of the SIS-CLassification and Data Analysis Group (SIS-CLADAG). His research interests include cluster analysis, mixture models, and latent variable models.