Everitt / Landau / Leese Cluster Analysis

5. Auflage 2010
ISBN: 978-0-470-97780-4
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 352 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

Cluster analysis comprises a range of methods for classifyingmultivariate data into subgroups. By organizing multivariate datainto such subgroups, clustering can help reveal the characteristicsof any structure or patterns present. These techniques have provenuseful in a wide range of areas such as medicine, psychology,market research and bioinformatics.
This fifth edition of the highly successful ClusterAnalysis includes coverage of the latest developments in thefield and a new chapter dealing with finite mixture models forstructured data.
Real life examples are used throughout to demonstrate theapplication of the theory, and figures are used extensively toillustrate graphical techniques. The book is comprehensive yetrelatively non-mathematical, focusing on the practical aspects ofcluster analysis.
Key Features:
* Presents a comprehensive guide to clustering techniques,with focus on the practical aspects of cluster analysis.
* Provides a thorough revision of the fourth edition,including new developments in clustering longitudinal data andexamples from bioinformatics and gene studies
* Updates the chapter on mixture models to include recentdevelopments and presents a new chapter on mixture modeling forstructured data.
Practitioners and researchers working in cluster analysis anddata analysis will benefit from this book.

Autoren/Hrsg.

Everitt, Brian S.

Landau, Sabine

Leese, Morven

Stahl, Daniel

Inhaltsverzeichnis

Preface
Acknowledgement
1 An introduction to classification and clustering
1.1 Introduction
1.2 Reasons for classifying
1.3 Numerical methods of classification - cluster analysis
1.4 What is a cluster?
1.5 Examples of the use of clustering
1.6 Summary
2 Detecting clusters graphically
2.1 Introduction
2.2 Detecting clusters with univariate and bivariate plots of data
2.3 Using lower-dimensional projections of multivariate data for graphical representations
2.4 Three-dimensional plots and trellis graphics
2.5 Summary
3Measurement of proximity
3.1 Introduction
3.2 Similarity measures for categorical data
3.3 Dissimilarity and distance measures for continuous data
3.4 Similarity measures for data containing both continuous and categorical variables
3.5 Proximity measures for structured data
3.6 Inter-group proximity measures
3.7 Weighting variables
3.8 Standardization
3.9 Choice of proximity measure
3.10 Summary
4Hierarchical clustering
4.1 Introduction
4.2 Agglomerative methods
4.3 Divisive methods
4.4 Applying the hierarchical clustering process
4.5 Applications of hierarchical methods
4.6 Summary
5Optimization clustering techniques
5.1 Introduction
5.2 Clustering criteria derived from the dissimilarity matrix
5.3 Clustering criteria derived from continuous data
5.4 Optimization algorithms
5.5 Choosing the number of clusters
5.6 Applications of optimization methods
5.7 Summary
6Finite mixture densities as models for cluster analysis
6.1 Introduction
6.2 Finite mixture densities
6.3 Other finite mixture densities
6.4 Bayesian analysis of mixtures
6.5 Inference for mixture models with unknown number of components and model structure
6.6 Dimension reduction - variable selection in finite mixture modelling
6.7 Finite regression mixtures
6.8 Software for finite mixture modelling
6.9 Some examples of the application of finite mixture densities
6.10 Summary
7Model-based cluster analysis for structured data
7.1 Introduction
7.2 Finite mixture models for structured data
7.3 Finite mixtures of factor models
7.4 Finite mixtures of longitudinal models
7.5 Applications of finite mixture models for structured data
7.6 Summary
8Miscellaneous clustering methods
8.1 Introduction
8.2 Density search clustering techniques
8.3 Density-based spatial clustering of applications with noise
8.4 Techniques which allow overlapping clusters
8.5 Simultaneous clustering of objects and variables
8.6 Clustering with constraints
8.7 Fuzzy clustering
8.8 Clustering and artificial neural networks
8.9 Summary
9Some final comments and guidelines
9.1 Introduction
9.2 Using clustering techniques in practice
9.3 Testing for absence of structure
9.4 Methods for comparing cluster solutions
9.5 Internal cluster quality, influence and robustness
9.6 Displaying cluster solutions graphically
9.7 Illustrative examples
9.8 Summary
Bibliography
Index