Clustering Algorithms

Clustering Algorithms......Page 1
Preface......Page 11
Contents......Page 13
I.1. Examples of Clustering......Page 19
I.3. Functions of Clustering......Page 24
I.4. Statistics and Data Analysis......Page 25
I.5. Types of Data......Page 27
I.7. Algorithms......Page 29
I.8. Interpretation and Evaluation. of Clusters......Page 30
I.9. Using This Book......Page 31
References......Page 32
1.1. Introduction......Page 46
1.3. Profiles of City Crime......Page 47
1.4. Rank Profiles Algorithm......Page 50
1.5. Linearly Optimal Profiles Algorithm......Page 52
1.6. Linearly Optimal Profiles of Crime Data......Page 54
1.7. Things To Do......Page 56
Programs......Page 58
2.2. Euclidean Distances......Page 76
2.3. Relations Between Variables......Page 80
2.5. Other Distances......Page 82
2.6. Plotting Distances To Detect Clusters......Page 83
2.7. Things To Do......Page 84
References......Page 85
Programs......Page 87
3.1. Introduction......Page 92
3.2. Leader Algorithm......Page 93
3.3. Leader Algorithm Applied To Jigsaw Puzzle......Page 94
3.4. Properties of Leader Algorithm......Page 95
3.5. Sorting Algorithm......Page 96
3.6. Sorting Algorithm Applied To Jigsaw Puzzle......Page 97
3.8. Things To Do......Page 98
Programs......Page 101
4.1. Introduction......Page 102
4.2. K-means Algorithm......Page 103
4.3. K-means Applied To Food Nutrient Data......Page 105
4.4. Analysis of Variance......Page 107
4.5. Weights......Page 109
4.6. Other Distances......Page 110
4.7. The Shape of K-means Clusters......Page 111
4.8. Significance Tests......Page 115
4.9. Things To Do......Page 118
References......Page 123
Programs......Page 125
5.1. Introduction......Page 131
5.2. Normal Mixture Algorithm......Page 134
5.3. Normal Mixture Algorithm Applied To New Haven School Scores......Page 136
5.4. Things To Do......Page 138
Programs......Page 143
6.2. Fisher Algorithm......Page 148
6.3. Fisher Algorithm Applied To Olympic Times......Page 150
6.4. Significance Testing and Stopping Rules......Page 153
6.5. Time and Space......Page 155
6.6. Things To Do......Page 156
References......Page 158
Programs......Page 159
7.2. Ditto Algorithm......Page 161
7.3. Application of Ditto Algorithm To Wines......Page 163
7.4. Things To Do......Page 165
Programs......Page 166
8.1. Definition of a Tree......Page 173
8.4. Naming Clusters......Page 174
8.6. I Representation of Animai Clusters......Page 175
8.8. Linear Representations of Trees......Page 176
8.9. Trees and Distances......Page 178
8.10. Block Representations of Trees......Page 180
8.11. Things To Do......Page 181
References......Page 182
Programs......Page 183
9.2. Leader Algorithm For Trees......Page 187
9.3. Tree-leader Algorithm Applied To Mammals' Teeth......Page 189
9.4. Things To Do......Page 190
Programs......Page 194
10.2. Triads Algorithm......Page 195
10.3. Triads Algorithm Applied to Hardware......Page 197
10.5. Triads-leader Algorithm......Page 199
10.6. Application of Triads-leader Algorithm To Expectation of Life......Page 200
10.7. Remarks On Triads-leader Algorithm......Page 202
10.8. Things To Do......Page 203
Programs......Page 205
11.2. Single-linkage Algorithm......Page 209
11.3. Application of Single-linkage Algorithm To Airline Distances......Page 211
11.4. Computational Properties of Single Linkage......Page 213
11.6. Application of Spiral Search Algorithm To Births and Deaths......Page 214
11.8. Joining and Splitting......Page 217
11.10. Strung-out Clusters......Page 218
11.11. Minimum Spanning Trees......Page 219
11.12. Reality of Clusters......Page 220
11.13. Density-contour Tree......Page 223
11.14. Densities and Connectedness, Distances Given......Page 226
11.15. Things To Do......Page 227
References......Page 230
Programs......Page 232
12.2. Joining Algorithm......Page 234
12.3. Joining Algorithm Applied To Ivy League Football......Page 236
12.5. Adding Algorithm......Page 240
12.6. Adding Algorithm Applied To Questionnaire......Page 241
12.7. Things To Do......Page 245
Programs......Page 248
13.2. Minimum Mutation Fits......Page 251
13.3. Application of Minimum Mutation Algorithm To Cerci In Insects......Page 253
13.4. Some Probability Theory For the Number of Mutations......Page 254
13.5. Reduced Mutation Tree......Page 255
13.6. Application of Reduced Mutation Algorithm To Amino Acid Sequences......Page 257
13.7. Things To Do......Page 259
References......Page 263
Programs......Page 264
14.1. Introduction......Page 269
14.2. Binary Splitting Algorithm......Page 270
14.3. Application of Binary Splitting Algorithm To Voting Data With Missing Values......Page 273
14.4. One-way Splitting Algorithm......Page 275
14.5. One-way Splitting Algorithm Applied To Republican Percentages......Page 276
14.6. Two-way Splitting Algorithm......Page 278
14.7. Two-way Splitting Algorithm Applied To Republican Vote for President......Page 280
14.8. Things To Do......Page 282
References......Page 286
Programs......Page 287
15.1. lntroduction......Page 296
15.2. Two-way Joining Algorithm......Page 298
15.3. Applicàtion of Two-way Joining Algorithm To Candida......Page 300
15.4. Generalizations of Two-way Joining Algorithm......Page 302
15.5. Significance Tests for Outcomes of Two-way joining Algorithm......Page 303
15.6. Ditect Joining Algorithm for Variables on Different Scales......Page 304
15.7. Things To Do......Page 306
Programs......Page 311
16.1. Introduction......Page 317
16.2. Scaling Ordered Variables......Page 318
16.3. Scaling Ordered Variables Applied To U.N. Questions......Page 320
16.4. Joiner-scaler......Page 321
16.5. Application of Joiner-scaler Algorithm To U.N. Votes......Page 323
16.6. Things To Do......Page 326
References......Page 330
17.1. Introduction......Page 331
17.2. Sparse Root Algorithm......Page 332
17 4. Remarks on the Sparse Root Algorithm......Page 334
17.5. Rotation to Simple Structure......Page 336
17.6. Joining Algorithm for Factor Analysis......Page 337
17.7. Application of Joining Algorithm To Physical Measurements Data......Page 338
17.8. Things To Do......Page 341
References......Page 343
Programs......Page 344
18.1. Introduction......Page 348
18.2. Variance Components Algorithm......Page 350
18.3. Variance Components Algorithm Applied To Prediction of Leukemia Mortality Rates......Page 351
18.5. Automatic Interaction Detection......Page 355
18.6. Application of AID Algorithm To Leukemia Mortality......Page 356
18.7. Remarks On The AID Algorithm......Page 358
18.8. Things To Do......Page 359
Programs......Page 361
INDEX......Page 365