Cluster Analysis

Informationen zum Autor Brian S. Everitt , Head of the Biostatistics and Computing Department and Professor of Behavioural Statistics, Kings College London. He has authored/ co-authored over 50 books on statistics and approximately 100 papers and other articles, and is also joint editor of Statistical Methods in Medical Research . Dr Sabine Landau , Head of Department of Biostatistics, Institute of Psychiatry, Kings College London. Dr Morven Leese , Health Service and Population Research, Institute of Psychiatry, Kings College London. Dr Daniel Stahl , Deptartment of Biostatistics & Computing, Institute of Psychiatry, Kings College London. Klappentext Cluster Analysis: 5th Edition Brian S. Everitt , Professor Emeritus, King's College, London, UK Sabine Landau, Morven Leese and Daniel Stahl , Institute of Psychiatry, King's College London, UK Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics. This 5th edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data. Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster 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 and examples from bioinformatics and gene studies ¿ Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data. Practitioners and researchers working in cluster analysis and data analysis will benefit from this book. Zusammenfassung Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster 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 and examples from bioinformatics and gene studies* Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data.Practitioners and researchers working in cluster analysis and data analysis will benefit from this book. Inhaltsverzeichnis Preface. Acknowledgement. 1 An Introduction to classification and clustering. 1.1 Introduction. 1.2 Reasons for classifying. 1.3 Numerical methods of classification ...

List of contents

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.
 
1.5.1 Market research.
 
1.5.2 Astronomy.
 
1.5.3 Psychiatry.
 
1.5.4 Weather classification.
 
1.5.5 Archaeology.
 
1.5.6 Bioinformatics and genetics.
 
1.6 Summary.
 
2 Detecting clusters graphically.
 
2.1 Introduction.
 
2.2 Detecting clusters with univariate and bivariate plots of data.
 
2.2.1 Histograms.
 
2.2.2 Scatterplots.
 
2.2.3 Density estimation.
 
2.2.4 Scatterplot matrices.
 
2.3 Using lower-dimensional projections of multivariate data for graphical representations.
 
2.3.1 Principal components analysis of multivariate data.
 
2.3.2 Exploratory projection pursuit.
 
2.3.3 Multidimensional scaling.
 
2.4 Three-dimensional plots and trellis graphics.
 
2.5 Summary.
 
3 Measurement of proximity.
 
3.1 Introduction.
 
3.2 Similarity measures for categorical data.
 
3.2.1 Similarity measures for binary data.
 
3.2.2 Similarity measures for categorical data with more than two levels.
 
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.
 
3.6.1 Inter-group proximity derived from the proximity matrix.
 
3.6.2 Inter-group proximity based on group summaries for continuous data.
 
3.6.3 Inter-group proximity based on group summaries for categorical data.
 
3.7 Weighting variables.
 
3.8 Standardization.
 
3.9 Choice of proximity measure.
 
3.10 Summary.
 
4 Hierarchical clustering.
 
4.1 Introduction.
 
4.2 Agglomerative methods.
 
4.2.1 Illustrative examples of agglomerative methods.
 
4.2.2 The standard agglomerative methods.
 
4.2.3 Recurrence formula for agglomerative methods.
 
4.2.4 Problems of agglomerative hierarchical methods.
 
4.2.5 Empirical studies of hierarchical agglomerative methods.
 
4.3 Divisive methods.
 
4.3.1 Monothetic divisive methods.
 
4.3.2 Polythetic divisive methods.
 
4.4 Applying the hierarchical clustering process.
 
4.4.1 Dendrograms and other tree representations.
 
4.4.2 Comparing dendrograms and measuring their distortion.
 
4.4.3 Mathematical properties of hierarchical methods.
 
4.4.4 Choice of partition - the problem of the number of groups.
 
4.4.5 Hierarchical algorithms.
 
4.4.6 Methods for large data sets.
 
4.5 Applications of hierarchical methods.
 
4.5.1 Dolphin whistles - agglomerative clustering.
 
4.5.2 Needs of psychiatric patients - monothetic divisive clustering.
 
4.5.3 Globalization of cities - polythetic divisive method.
 
4.5.4 Women's life histories - divisive clustering of sequence data.
 
4.5.5 Composition of mammals' milk - exemplars, dendrogram seriation and choice of partition.
 
4.6 Summary.
 
5 Optimization clustering techniques.
 
5.1 Introduction.
 
5.2 Clustering criteria derived from the dissimilarity matrix.
 
5.3 Clustering criteria derived from continuous data.
 
5.3.1 Minimization of trace(W).
 
5.3.2 Minimization o

Product details

• Authors: Everitt, Brian Everitt, Brian S Everitt, Brian S. Everitt, Brian S. Landau Everitt, Sabin Landau, Sabine Landau, Landau Sabine, Morven Leese, Morven et a Leese, Leese Morven, Daniel Stahl, Stahl Daniel
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 07.01.2011
• EAN: 9780470749913
• ISBN: 978-0-470-74991-3
• No. of pages: 346
• Dimensions: 157 mm x 235 mm x 24 mm
• Series: Wiley Series in Probability and Statistics; Wiley Series in Probability and Statistics
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Statistik, Datenanalyse, Data Mining, Statistics, data analysis, Longitudinal Analysis, Data Mining Statistics, Longitudinalanalyse