|
|
 |
| Авторизация |
|
|
|
| Поиск по указателям |
|
|
|
|
|
|
|
 |
|
|
|
| Jain A.K., Dubes R.C. — Algorithms for clustering data |
|
|
|
| Предметный указатель |
Monte Carlo studies with MH statistic 187
Monte Carlo studies, in comparative analysis 139
Monte Carlo tests of cluster analysis 140
Monte Carlo trials 156 159
Moore, L. R. 159
Moreau, J. V. 160
MST see Minimum spanning tree
MST-based clustering in image segmentation 233
Mucciardi, A. N. 119
MUI 1
Mui, K. 226 243
Muitiband imagery data 40
Multidimensional scaling 46-53
Multidimensional scaling, in clustering methodology 136
Multivariate analysis of variance 264
Multivariate normality 247
Mutual nearest neighbor clustering in image segmentation 233
Mutual neighborhood clustering algorithm 129
Mutual neighborhood value 129
Naming axes in MDSCAL 52
Narendra, P. 3 119 235 243
Naus, J. I. 212
Near-neighbor distribution and sampling window 219
Near-neighbor distribution for hard-core models 210
Near-neighbor distribution, to estimate intrinsic dimensionality 46
Nearest neighbor and missing data 19
Nearest neighbor distance 129
Nearest neighbor distance and tests for randomness 215
Nearest neighbor distance, pattern-to-pattern 217
Nearest-neighbor clustering algorithms 128
Nearest-neighbor decision rule 3
Nearest-neighbor density estimation 118
Nearest-neighbor method, pattern density 120
Nearest-neighbor rule in pattern recognition 242
Nearest-neighbor, shared 129
Neighborhood depth 129
Neighborhood size 129
Nested partition 58
Neyman - Scott process 207
Neyman, J. 207 273
Ni, L. 101
Nicholls, P. 59
Niemann, H. 39
Node coloring 71
Node coloring and complete-link clustering 72
Node connectivity 86
Node degree 86
Node degree and test for randomness 221
Nominal data type 16
Nominal scale 12
Nonlinear projection 37-42
Nonparametric maximum likelihood estimation 160
Nonrandom structure, test for 162
Norma1 distribution, sampling 159
Normality of Hubert’s  statistic 153
Normalization of data 23-25
Null distribution, Hubert’s statistic 175
Null population, eightyX data 164
Number of clusterings 90
Number of clusters, from clustering algorithms 138
Number of clusters, from DB statistic 186
Number of clusters, from knee in square-error curve 105
Number of clusters, from MH statistic 187
Number of components in graph 272
Number of features and intrinsic dimensionality 43
Numerical taxonomy 167
Object detection via image registration 238
Odell, P. L. 5 117
Ogata, Y. 202 211
Ogilvie, J. C. 139 167
Ohlander, R. 226
Okada 36
Olsen, D. R. 45
Ordination 47
Orthogonal matrix 253
Osmer, P. S. 211
Outlier, in comparative analysis 138
Outlier, in FORGY program 101
Outlier, in hierarchical clustering 81
Outlier, in MDSCAL program 51
Outlier, in partitional clustering 98
Overlap of clusters 274
Overlapping classification 56
Overlapping clusters and fuzzy clustering 131
Packing density 208
Pair group method 79
Panayirci, E. 202 217 218
Panayirci, E., 219
Parallel processing and partitional clustering 1
Parametric classification 243
Partition 58
Partition function for Gibbs process 211
Partition ranks 168
Partitional classification 57
Partitional clustering 89-133
Partitional clustering and image segmentation 226-27
Partitional clustering Forgy’s method 97
Partitional clustering method, iterative 96
Partitional clustering of textured image 227
Partitional clustering problem, statement 90
Partitional clustering, fuzzy algorithm 133
Partitional clustering, iterative algorithm 96
Partitional clustering, McQueen’s K-means method 97
Parzen window 118 120
Patrick, E. A. 129
Pattern 8
Pattern matrix 8
Pattern recognition 241-45
Pattern space 8
Peay, E, R. 65
Perceived similarity 11
Percentile frame 221
Perceptual grouping 41
Perfect cluster 190
Perfect hierarchical structure 68
Perfect hierarchical structure and ultrametric inequality 83
Periodic boundaries 207-208
Permutation and random label hypothesis 144- 45
Permutation statistic 22
Permutation with Hubert’s statistic 148-50
Pettis, K. 44 46
Pietou, E. C. 212
Pixel 225
Point serial correlation 148
Point serial correlation coefficient 186
Poisson process and random position hypothesis 145
Poisson process, as spatial point process 203
Poisson process, in clustering tendency 219
Poisson process, in quadrat analysis 212
Poisson process, nearest-neighbor distribution 218
Poisson random variable, sampling 159
Pollard, D. 100
Population correlation coefficient 247
Population moments 247
Positive-definite matrix and proximity index 17
Potential function 211
Power of test of hypothesis 146-48
Pratt, J. W. 250
Preparata, F. P. 125
Prim, R, C. 271
Prim’s algorithm 271
Principal component, in factor analysis 260
Principal component, in factor analysis, projection 26
Principal component, in factor analysis, significance from bootstrapping 160
Probabilistic index of similarity 20
Probability density estimate 118
Probability profile 195-96
Processing time for partitional clustering 101
Product moment correlation coefficient 166
Projection algorithm 25
| Projection by discriminant analysis 35
Projection pursuit algorithm 42
Proximity dendrogram 62 66
Proximity graph 66
Proximity index 14 23
Proximity index and missing data 19
Proximity index, binary 12
Proximity index, continuous 12
Proximity index, discrete 12
Proximity index, dissimilarity 11
Proximity index, matching coefficient 16
Proximity index, nominal type 16
Proximity index, ratio type 14
Proximity index, similarity 11
Proximity matrix 11
Proximity matrix, ordinal 145
Proximity matrix, rank order 145
Proximity matrix, symmetry 11
Proximity ranks 168
Proximity, cophenetic see Cophenetic proximity
Pseudorandom samples 159
Pykett, N. E. 39
Q-mode clustering 9
Quadrat analysis 212
Qualitative scale 12
Quantitative scale 12
Questionnaire data 17 20 23
R-chain 87
R-connected subgraph 87
R-mode clustering 9
Rabiner, L. R. 223
Radius of subgraph 87
Rafsky, L. C. 59
Rammal, R. 69
Ramsay, J. O. 52 53
Rand statistic 174
Rand statistic, corrected for chance 175-77
Rand statistic, in comparative analysis 140
Rand, W. 174
Random graph 272
Random graph and cluster validity 194
Random graph hypothesis and global fit of hierarchy 167
Random graph hypothesis and internal indices of cluster validity 192-94
Random graph hypothesis with external indices of cluster validity 189
Random graph hypothesis with MDSCAL 51
Random graph hypothesis, and cluster lifetime 197
Random graph hypothesis, in testing hypothesis 144-45 149
Random label hypothesis with Hubert’s statistic 151 53
Random label hypothesis, and internal index of cluster validity 163
Random label hypothesis, in testing hypothesis 144 45 149
Random position hypothesis, and 80X data 164
Random position hypothesis, and 80X data and spatial point process 202-203
Random position hypothesis, and 80X data and testing hypothesis 144 45
Random threshold graph 272
Random variable 144
Rank correlation 166-67
Rank graph 272
Rank order proximity matrix and  statistic 153
Rank, between-group scatter matrix 36
Rao, C. R 223
Rao, M. R. 91
Rao, V. R. 179
Rapoport, A. 221
Rasson, J. P. 207
Ratio scale 13
Reference population and validity of hierarchy 166
Reference population for random graph hypothesis 145
Region of influence 123
Register, D. 119
Regression lines, fit from bootstrapping 160
Regularity and spatial clustering 207
Regularly spaced patterns 202
Relative criteria 161
Relative error in Monte Carlo analysis 157
Relative index for clusters 200
Relative index for global fit of hierarchy 170
Relative index for partitional adequacy 183-88
Relative index, and best-case index 195
Relative neighbor 124
Relative neighbor graph and partitions 91
Relative neighbor graph, and clustering tendency 214
Relative neighbor graph, definition 123-24
Remote-sensing 117 119 223-25 227
Remote-sensing, resampling 159
Remote-sensing, residual correlations 261
Remote-sensing, reversal 84 86
Renyi, A. 221
Ripley, B. D. 202 203 207 208 210 211 212 214
Ripley’s K(t) function 212
Rogers, A. 212
Rohlf, F. J. 71 80 119 166
Rohlf, F. J., 167
Romney, A. K. 47 52
Rosenfeld, A. 224
Ross, G. J. S. 71
Rubin, I. 34 94 97 108 138
Ruspini, E. H. 131
S clustering package 134
S-statistic 23
SAHN clustering algorithms 57
SAHN clustering algorithms and graph theory 87
SAHN clustering algorithms, monotonicity 84
SAHN clustering algorithms, table of coefficients 80
SAHN clustering algorithms, updating formula 79
Salton, G. 223
Sammon, J. W. 38
Sammon’s nonlinear projection 38
Sample correlation coefficient 16 148
Sample covariance matrix 16 252
Sample space 144
Sampling frame 207
Sampling origin 217-18
Sampling window and internal index of partitional adequacy 178
Sampling window and spatial point processes 202-3
Sampling window, in clustering tendency 220
Sampling window, in regular spatial point process 208
Sampling window, shape and size 207
Sanderson, A. C. 223
Saunders, R. 214
Scale, interval 13
Scale, nominal 12
Scale, ordinal 153
Scale, quantitative 153
Scale, ratio 13
Scaling by range 24
Scaling of features 24
Scan tests for randomness 211
Scatter matrix, decomposition 94
Scatter matrix, definitions 258-59
Scatter matrix, in partitional clustering 94
Scatter ratio 35
Scatter ratio, asymptotic Gaussian distribution for 182
Scatter, between-group 35
Scatter, between-group within-group 35
Scatter, between-group, geometrical interpretation 34
Scatter, between-group, maximizing 34
Scene analysis 224
Schachter, B. J. 40 228
Scher, A. 223
Schikhof, W. H. 69
Schilling, D. A. 140
Schultz, J. R. 148 153 175 221
Schwartzmann, D. H. 44 45
Scott, A. J. 117
Scott, E. L. 207 273
Sdim, S. Z. 99
Sdove, S. L. 117
Sebestyen, G. S. 34 119
Seed points 97
Seed points, in FORGY 101
|
|
|
| Реклама |
|
|
|
|
|
О проекте