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| Jain A.K., Dubes R.C. — Algorithms for clustering data |
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| Предметный указатель |
Hierarchical clustering and sequence of nested partitions 59
Hierarchical partition 57
Hierarchical structure and ultrametricity 68
Hierarchical structure, perfect 68
Hierarchical structure, true 69
Hierarchical tree classifier 243
hierarchies 160
Hill-climbing 91
Hill-climbing pass 97
Hines, R. J, O. 211
Hines, W. C. S. 211
Histogram of edge lengths in MST gray-level 225
Histogram of Hubert’s  for 80X data 163
Histogram of nearest-neighbor distances 218
Histogram with Hubert’s statistic 149-50
Histogram, in density estimation 119
Histogram, shift in, from random label to random position hypotheses 165
Hoffman, R. 214 224 232 233 235
Hopkins statistic 218
Hopkins, B. 211 218
Hord, R 225 235
Hough transform 240
Howe, S. E. 125
Hubalek, Z. 17
Huber, P. J. 42
Hubert, L. J. 11 53 63 64 72 79 86 141 148 153 167 168 170 172 175 176 193 221
Hubert’s algorithm, for complete-link clustering 63
Hubert’s algorithm, for complete-link clustering for single-link clustering 63
Hubert’s gamma () statistic 148
Hubert’s gamma () statistic and degree oflinearcorrespondence 149
Hubert’s gamma () statistic and validity of hierarchy 166
Hubert’s gamma () statistic with stopping rule 185
Hubert’s gamma () statistic, 80X data, example 151
Hubert’s gamma () statistic, external criterion, example 162
Hubert’s gamma () statistic, in comparative analysis 140
Hubert’s gamma () statistic, in partitional adequacy 174
Hubert’s gamma () statistic, internal criterion, example 162
Hubert’s gamma () statistic, modification for relative index 186
Hubert’s gamma () statistic, normalized 148
Hypergeometric distribution 249
Hypergeometric distribution and external indices of cluster validity 189-90
Hypergeometric distribution and external indices of partitional adequacy 175
Hypergeometric distribution and permutation statistic 23
Hypergeometric distribution and probability profiles 195
Hypergeometric probability, computing 250
Hypergeometric probability, computing, Peizer approximation to 251
Hypersphere, distribution of distances in 44
Hypothesis, alternative 146 148
Hypothesis, alternative, null 144-47
Hypothesis, alternative, null, for Hubert’s gamma () statistic 149
Hypothesis, alternative, random graph 144 45 149
Hypothesis, alternative, random label 144 45 149
Hypothesis, alternative, random position 144-45
Hypothesis, alternative, randomness 144
Hypothesis, alternative, randomness, and internal index of partitional adequacy 179
Hypothesis, alternative, testing 144
ICICLE package 134
Identifiable mixture 117
Image classification 226
Image classification, image processing 224
Image registration 237
Image segmentation and clustering 225
Ince, F. 235
Inconsistent edges 121
Inconsistent edges and sparse clusters 123
Inconsistent edges and structure graphs 128
Inconsistent edges, in range image segmentation 233
Index of cluster validity 160-65
Index of partitional adequacy 174
Index of proximity 11
Index of structure 147
Index, compared to criterion 161
Index, for comparing partitions 172
Indicator function 173
INDSCAL program 53
Information measure for contingency table 20
Initial partition, in iterative partitional clustering 96
Initial partition, in square-error clustering 97
Initial partition, recovery from 98
Intensity of spatial point process 212
Internal edges, as compactness index 189-90
Internal edges, in best-case indices 194
Internal index for clusters 192
Internal index for global fit of hierarchies 166
Internal index of partitional adequacy 177
Internal index, as relative index of partitional adequacy 178
Internal index, in clustering methodology 137
Interpoint distance and test for randomness 213
Interpoint distance, in intrinsic dimensionality 45
Interval estimator 157
Interval scale 13
Intrinsic character of data 160
Intrinsic classification 56
Intrinsic dimensionality 42 46
Intrinsic dimensionality, Bennett’s method 44
Intrinsic dimensionality, estimation from near-neighbor information 46
Intrinsic dimensionality, global approach 44
Intrinsic dimensionality, in clustering methodology 136
Intrinsic dimensionality, local approach 45
Intrinsic dimensionality, Trunk’s method 46
Isaac, P. D. 139
Isham, V. 203 211
Ismail, M. A. 99
ISODATA, and nearest-neighbor computation 3
ISODATA, description 98
ISODATA, fuzzy 132
ISODATA, in remote sensing 237
ISODATA, parallel computation 101
Isolated cluster 192
Isolation index 189-90
Isolation index, best case 194
Isolation of cluster 188-90
ISPAHAN 135
Iterative partitional clustering algorithm 96
Ittner, D. 232
Jaccard coefficient 17
Jaccard coefficient for binary vectors 21
Jaccard statistic 174
Jain, A. K. 6 96 97 101 108 132 137 138 160 177 202 203 215 218 221 223 224 227 231 232 233 235 243 245 247
Jardine, N. 5 64 69 77 38
Jarvis, R. A. 129
Jensen, R. E. 91
Johnson, I. E. 159
Johnson, S. C. 65 72
Johnston, B. 133
Joins 214
Journal of classification 4
Julesz, B. 227
K - MEANS algorithm in comparative analysis 140
K - MEANS algorithm in comparative analysis, means method, implementation 134
K - MEANS algorithm in comparative analysis, means pass 97 109
Kak 224
Kakusho 53
Kamel 210
Kamenskii, V. S. 47
Kamgar - Parsi, B. 129
Kanal, L. N. 6 129
Karhunen - Loeve projection 26
Katz, J. O. 119
Kelly, F. P. 214
Kempthome, O. 23
Kendall, M. G. 56
Kendal’s  statistic 167
Kernel function 120
Killough, G. S. 221 222
King, B. 72
Kirkpatrick, S. 39
Kittler, J. 19 91 119 120 147 243
Klahr, D 51
Kleiner, B. 38 59
| Knee with eigenvector projection 27
Knee, in curve of average error 179
Knee, significant, in relative index of validity 187
Knoll, R. L. 51
Knuth, D. E. 159
Kolmogorov - Smirnov statistic 213
Koontz, W. L. G. 37 91 119 123 133
Korfhage, R. R 266
Krishna, G. 92 129 130
Krishnaiah, P. R. 6
Kruskal, J. B. 39 47 51 52
Kruskal, J. B., 53
Kruskal’s stress 47
Kruskat, W. H. 16 153
Krzanowski, W. J. 27
Kuiper, F. K. 139
Lachenbruch, P. A. 34 242
Lance, G. N. 56 79 80 86
Landis, D. 53
LANDSAT image 235
Lane 120
Lattice regularity 208
Lee, D. 125
Lee, R. 6 40 41
Lefkovitch, L. P. 91
Legendre, P. 15
Lesaffre, E. 247
Level function 66
Level of significance in Monte Carlo analysis, J 58
Level of test of hypothesis 146
Level, critical 146
Levine, D. I., II 12 48 51 52
Levine, M. D. 119 131
Lewis 218 219
Li, X. 20 22 23 238 251
Liebetrau, A. 212
Lifetime of cluster 197-98
Light stripping 232
Likelihood function 211
Lindman, H. 53
Linear algebra 252-57
Linear dimensionality 43
Linear projection 25-36
Ling index 198
Ling, R. F. 87 88 145 197 221 222 250
Lingoes, J. C. 48
Linking edge, as isolation index 189-90
Linking edge, in best-case index 194
Liu, T. S. 119
Local clustering criterion 90
Local minimum in MDSCAL 52
Local minimum, effect of initial partition 97
Lohnes, P. R. 81 179 264
Lorr 5
Lu, S. Y. 128 129 223
Lumelsky, V. 24
MacCailum, R. C. 53
Machine vision 224
Magnuski, H. S. 123
Mahaianobis distance, definition 16
Mahaianobis distance, in Gaussian distribution 249
Mahaianobis distance, in square-error clustering 93
Mahtab, M. A. 223
Mallows, C. L. 140 170 174 175
Mandelbrot, A. B. 43
Manhattan distance 15
Manova see Multivariate Analysis of Variance
Mantel statistic 148
Mantel statistic, E 53
Mantel, N. 148 153
Mantock, J. 37
Marriott, F. H. C. 138 158
Matching coefficient 16
Matrix updating algorithm 79
Matrix updating algorithm and monotonicity 79
Matrix updating algorithm, complete-link 72
Matrix updating algorithm, effect of ties 78
Matrix updating algorithm, example 73
Matrix updating algorithm, single-link 72
MatuJa, D. W. 65 123 200
Matula index 200
Maxima1 subgraph 64
Maximal complete subgraph 269
Maximizing scatter 34
Maximum method 65
McClain, J. O. 179
McLachlan, G. J. 117
McLaughlin, B. 139
McQueen, J. B. 97
McQueen’s K-means method 97
MDSCAL, algorithm 47
MDSCAL, algorithm and hierarchical clustering 52
MDSCAL, algorithm and Sammon’s nonlinear projection 39
MDSCAL, algorithm, interpreting configurations 51
MDSCAL, algorithm, naming axes 52
MDSCAL, algorithm, program 50
Mead, R. 212
Measurement space 2
Median method 80
Meehl, P. E. 138
Metropolis algorithm 212
Metropolis, N. 211
MH-Modified Hubert’s statistic 187
Michalski, R. S. 92 224
Milligan, G. W. 79 84 138 139 140 141 153 175 177 179 185 188
Minimum mean-square-error projection 31
Minimum method 65
Minimum spanning tree and DeLaunay triangulation 125
Minimum spanning tree and Gestalt principle 121
Minimum spanning tree and single-link clustering 70
Minimum spanning tree and triangulation 41
Minimum spanning tree and unfolding data 45
Minimum spanning tree, definition 271
Minimum spanning tree, in clustering tendency 214
Minimum variance method 80
Minimum variance partition 93
Minkowski metric 14 15 47
Missing data 19
Mitchell, O. R. 228
Mitiche, A. 228
Mixture decomposition 117
Mixture density 117
Mizoguchi, R. 53 123
Mode separation 120
Mode-seeking 117-18
Mojena, R. 139
Moment of inertia 25
Monothetic clustering algorithm 58
Monotone methods and ultrametricity 83
Monotone regression 50
Monotonicity and crossovers in dendrogram 83
Monotonicity and matrix updating 79
Monotonicity and ultrametricity 70
Monotonicity and updating formula 84
Monotonicity with SAHN methods 80
Monotonicity, in SAHN algorithms 84
Monte Cario Analysis in hypothesis testing 145
Monte Cario Analysis in hypothesis testing with test of cluster validity 161
Monte Cario Analysis in hypothesis testing, in test for nonrandom structure 162
Monte Cario Analysis in hypothesis testing, in tests for randomness 215 218
Monte Carlo analysis 155-59
Monte Carlo analysis of baseline distribution for square-error 178
Monte Carlo analysis of CPCC 166
Monte Carlo analysis with Hubert’s statistic 150 153
Monte Carlo analysis, in clustering methodology 137
Monte Carlo estimate, crude 155 157
Monte Carlo sampling 155-56 158
Monte Carlo studies of external indices of partitional adequacy 176
Monte Carlo studies of stress distribution 51
Monte Carlo studies with DB statistic 186
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