Cluster analysis /
"This edition provides a thorough revision of the fourth edition which focuses on the practical aspects of cluster analysis and covers new methodology in terms of longitudinal data and provides examples from bioinformatics. Real life examples are used throughout to demonstrate the application o...
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| Other Authors: | |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Chichester, West Sussex :
Wiley,
2011.
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| Edition: | 5th ed. |
| Series: | Wiley series in probability and statistics.
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| Subjects: | |
| Online Access: | CONNECT CONNECT |
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| 245 | 0 | 0 | |a Cluster analysis / |c Brian S. Everitt [and others]. |
| 250 | |a 5th ed. | ||
| 260 | |a Chichester, West Sussex : |b Wiley, |c 2011. | ||
| 300 | |a 1 online resource (xii, 330 pages) : |b illustrations | ||
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| 520 | |a "This edition provides a thorough revision of the fourth edition which focuses on the practical aspects of cluster analysis and covers new methodology in terms of longitudinal data and provides examples from bioinformatics. Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. This book includes an appendix of getting started on cluster analysis using R, as well as a comprehensive and up-to-date bibliography"--Provided by publisher. | ||
| 504 | |a Includes bibliographical references (pages 289-320) and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a An introduction to classification and clustering -- Detecting clusters graphically -- Measurement of proximity -- Hierarchical clustering -- Optimization clustering techniques -- Finite mixture densities as models for cluster analysis -- Model-based cluster analysis for structured data -- Miscellaneous clustering methods -- Some final comments and guidelines. | |
| 590 | |a Wiley EBA | ||
| 650 | 0 | |a Cluster analysis. | |
| 650 | 0 | 2 | |a Cluster Analysis. |
| 650 | 0 | 2 | |a Multivariate Analysis. |
| 700 | 1 | |a Everitt, Brian. | |
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| 880 | 8 | |6 505-00/r |a 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 of det(W) -- 5.3.3 Maximization of trace (BW־¹) -- 5.3.4 Properties of the clustering criteria -- 5.3.5 Alternative criteria for clusters of different shapes and sizes -- 5.4 Optimization algorithms -- 5.4.1 Numerical example -- 5.4.2 More on k-means -- 5.4.3 Software implementations of optimization clustering -- 5.5 Choosing the number of clusters -- 5.6 Applications of optimization methods -- 5.6.1 Survey of student attitudes towards video games -- 5.6.2 Air pollution indicators for US cities. | |
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