Cluster analysis:
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Chichester
Wiley
2011
|
| Edition: | 5. ed. |
| Series: | Wiley series in probability and statistics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. [289] - 320 |
| Physical Description: | XII, 330 S. Ill., graph. Darst., Kt. |
| ISBN: | 9780470749913 9780470977811 |
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| 245 | 1 | 0 | |a Cluster analysis |c Brian S. Everitt ... |
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Record in the Search Index
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|---|---|
| adam_text | Titel: Cluster analysis
Autor: Everitt, Brian
Jahr: 2011
Contents
Preface xiii
Acknowledgement xv
1 An Introduction to classification and clustering 1
1.1 Introduction 1
1.2 Reasons for classifying 3
1.3 Numerical methods of classification - cluster analysis 4
1.4 What is a cluster? 7
1.5 Examples of the use of clustering 9
1.5.1 Market research 9
1.5.2 Astronomy 9
1.5.3 Psychiatry 10
1.5.4 Weather classification 11
1.5.5 Archaeology 12
1.5.6 Bioinformatics and genetics 12
1.6 Summary 13
2 Detecting clusters graphically 15
2.1 Introduction 15
2.2 Detecting clusters with univariate and bivariate plots
of data 16
2.2.1 Histograms 16
2.2.2 Scatterplots 16
2.2.3 Density estimation 19
2.2.4 Scatterplot matrices 24
2.3 Using lower-dimensional projections of multivariate data
for graphical representations 29
2.3.1 Principal components analysis of multivariate data 29
2.3.2 Exploratory projection pursuit 32
2.3.3 Multidimensional scaling 36
2.4 Three-dimensional plots and trellis graphics 38
2.5 Summary 41
Measurement of proximity 43
3.1 Introduction 43
3.2 Similarity measures for categorical data 46
3.2.1 Similarity measures for binary data 46
3.2.2 Similarity measures for categorical data with more
than two levels 47
3.3 Dissimilarity and distance measures for continuous data 49
3.4 Similarity measures for data containing both continuous
and categorical variables 54
3.5 Proximity measures for structured data 56
3.6 Inter-group proximity measures 61
3.6.1 Inter-group proximity derived from the proximity matrix 61
3.6.2 Inter-group proximity based on group summaries for
continuous data 61
3.6.3 Inter-group proximity based on group summaries for
categorical data 62
3.7 Weighting variables 63
3.8 Standardization 67
3.9 Choice of proximity measure 68
3.10 Summary 69
Hierarchical clustering 71
4.1 Introduction 71
4.2 Agglomerative methods 73
4.2.1 Illustrative examples of agglomerative methods 73
4.2.2 The standard agglomerative methods 76
4.2.3 Recurrence formula for agglomerative methods 78
4.2.4 Problems of agglomerative hierarchical methods 80
4.2.5 Empirical studies of hierarchical agglomerative methods 83
4.3 Divisive methods 84
4.3.1 Monothetic divisive methods 84
4.3.2 Polythetic divisive methods 86
4.4 Applying the hierarchical clustering process 88
4.4.1 Dendrograms and other tree representations 88
4.4.2 Comparing dendrograms and measuring their distortion 91
4.4.3 Mathematical properties of hierarchical methods 92
4.4.4 Choice of partition - the problem of the number of groups 95
4.4.5 Hierarchical algorithms 96
4.4.6 Methods for large data sets 97
4.5 Applications of hierarchical methods 98
4.5.1 Dolphin whistles - agglomerative clustering 98
4.5.2 Needs of psychiatric patients - monothetic divisive
clustering 101
4.5.3 Globalization of cities - polythetic divisive method 101
4.5.4 Women s life histories - divisive clustering
of sequence data 105
4.5.5 Composition of mammals milk - exemplars,
dendrogram seriation and choice of partition 107
4.6 Summary 110
5 Optimization clustering techniques 111
5.1 Introduction 111
5.2 Clustering criteria derived from the dissimilarity matrix 112
5.3 Clustering criteria derived from continuous data 113
5.3.1 Minimization of trace(W) 114
5.3.2 Minimization of det(W) 115
5.3.3 Maximization of trace (BW_1) 115
5.3.4 Properties of the clustering criteria 115
5.3.5 Alternative criteria for clusters of different
shapes and sizes 116
5.4 Optimization algorithms 121
5.4.1 Numerical example 124
5.4.2 More on fc-means 125
5.4.3 Software implementations of optimization clustering 126
5.5 Choosing the number of clusters 126
5.6 Applications of optimization methods 130
5.6.1 Survey of student attitudes towards video games 130
5.6.2 Air pollution indicators for US cities 133
5.6.3 Aesthetic judgement of painters 136
5.6.4 Classification of nonspecific back pain 141
5.7 Summary 142
6 Finite mixture densities as models for cluster analysis 143
6.1 Introduction 143
6.2 Finite mixture densities 144
6.2.1 Maximum likelihood estimation 145
6.2.2 Maximum likelihood estimation of mixtures
of multivariate normal densities 146
6.2.3 Problems with maximum likelihood estimation
of finite mixture models using the EM algorithm 150
6.3 Other finite mixture densities 151
6.3.1 Mixtures of multivariate ^-distributions 151
6.3.2 Mixtures for categorical data - latent class analysis 152
6.3.3 Mixture models for mixed-mode data 153
6.4 Bayesian analysis of mixtures 154
6.4.1 Choosing a prior distribution 155
6.4.2 Label switching 156
6.4.3 Markov chain Monte Carlo samplers 157
6.5 Inference for mixture models with unknown number
of components and model structure 157
6.5.1 Log-likelihood ratio test statistics 157
6.5.2 Information criteria 160
6.5.3 Bayes factors 161
6.5.4 Markov chain Monte Carlo methods 162
6.6 Dimension reduction - variable selection in finite mixture
modelling 163
6.7 Finite regression mixtures 165
6.8 Software for finite mixture modelling 165
6.9 Some examples of the application of finite mixture densities 166
6.9.1 Finite mixture densities with univariate Gaussian
components 166
6.9.2 Finite mixture densities with multivariate Gaussian
components 173
6.9.3 Applications of latent class analysis 177
6.9.4 Application of a mixture model with different
component densities 178
6.10 Summary 185
Model-based cluster analysis for structured data 187
7.1 Introduction 187
7.2 Finite mixture models for structured data 190
7.3 Finite mixtures of factor models 192
7.4 Finite mixtures of longitudinal models 197
7.5 Applications of finite mixture models for structured data 202
7.5.1 Application of finite mixture factor analysis to the
categorical versus dimensional representation debate 202
7.5.2 Application of finite mixture confirmatory factor
analysis to cluster genes using replicated
microarray experiments 205
7.5.3 Application of finite mixture exploratory factor
analysis to cluster Italian wines 207
7.5.4 Application of growth mixture modelling to identify
distinct developmental trajectories 208
7.5.5 Application of growth mixture modelling to identify
trajectories of perinatal depressive symptomatology 211
7.6 Summary 212
Miscellaneous clustering methods 215
8.1 Introduction 215
8.2 Density search clustering techniques 216
8.2.1 Mode analysis 216
8.2.2 Nearest-neighbour clustering procedures 217
8.3 Density-based spatial clustering of applications with noise 220
8.4 Techniques which allow overlapping clusters 222
8.4.1 Clumping and related techniques 222
8.4.2 Additive clustering 223
8.4.3 Application of MAPCLUS to data on social
relations in a monastery 225
8.4.4 Pyramids 226
8.4.5 Application of pyramid clustering to gene
sequences of yeasts 230
8.5 Simultaneous clustering of objects and variables 231
8.5.1 Hierarchical classes 232
8.5.2 Application of hierarchical classes to psychiatric
symptoms 234
8.5.3 The error variance technique 234
8.5.4 Application of the error variance technique to
appropriateness of behaviour data 237
8.6 Clustering with constraints 237
8.6.1 Contiguity constraints 240
8.6.2 Application of contiguity-constrained clustering 242
8.7 Fuzzy clustering 242
8.7.1 Methods for fuzzy cluster analysis 245
8.7.2 The assessment of fuzzy clustering 246
8.7.3 Application of fuzzy cluster analysis to Roman
glass composition 246
8.8 Clustering and artificial neural networks 249
8.8.1 Components of a neural network 250
8.8.2 The Kohonen self-organizing map 252
8.8.3 Application of neural nets to brainstorming sessions 254
8.9 Summary 255
9 Some final comments and guidelines 257
9.1 Introduction 257
9.2 Using clustering techniques in practice 260
9.3 Testing for absence of structure 262
9.4 Methods for comparing cluster solutions 264
9.4.1 Comparing partitions 264
9.4.2 Comparing dendrograms 265
9.4.3 Comparing proximity matrices 267
9.5 Internal cluster quality, influence and robustness 267
9.5.1 Internal cluster quality 268
9.5.2 Robustness - split-sample validation and consensus trees 269
9.5.3 Influence of individual points 271
9.6 Displaying cluster solutions graphically 273
9.7 Illustrative examples 278
9.7.1 Indo-European languages - a consensus tree in linguistics 279
9.7.2 Scotch whisky tasting - cophenetic matrices for
comparing clusterings 279
9.7.3 Chemical compounds in the pharmaceutical industry 281
9.7.4 Evaluating clustering algorithms for gene
expression data 285
9.8 Summary 287
Bibliography 289
Index 321
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| spelling | Cluster analysis Brian S. Everitt ... 5. ed. Chichester Wiley 2011 XII, 330 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Literaturverz. S. [289] - 320 Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Cluster-Analyse (DE-588)4070044-6 s DE-604 Everitt, Brian 1944- Sonstige (DE-588)121459411 oth Erscheint auch als Online-Ausgabe, EPUB 978-0-470-97844-3 Erscheint auch als Online-Ausgabe, PDF 978-0-470-97780-4 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020882615&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cluster analysis Cluster-Analyse (DE-588)4070044-6 gnd |
| subject_GND | (DE-588)4070044-6 |
| title | Cluster analysis |
| title_auth | Cluster analysis |
| title_exact_search | Cluster analysis |
| title_full | Cluster analysis Brian S. Everitt ... |
| title_fullStr | Cluster analysis Brian S. Everitt ... |
| title_full_unstemmed | Cluster analysis Brian S. Everitt ... |
| title_short | Cluster analysis |
| title_sort | cluster analysis |
| topic | Cluster-Analyse (DE-588)4070044-6 gnd |
| topic_facet | Cluster-Analyse |
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