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Jain A.K., Dubes R.C. — Algorithms for clustering data
Jain A.K., Dubes R.C. — Algorithms for clustering data

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Название: Algorithms for clustering data

Авторы: Jain A.K., Dubes R.C.

Язык: en

Рубрика: Математика/Вероятность/Статистика и приложения/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Издание: 1

Год издания: 1988

Количество страниц: 320

Добавлена в каталог: 04.06.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
80X data, description      8
80X data, discriminant analysis      36
80X data, eigenvector projection      28
80X data, examples of internal and external indices      162
80X data, factor analysis      28
80X data, first two principal components      28
80X data, listing      10
80X data, Sammon’s nonlinear projection      39
A priori information and external criteria      161
A priori labeling      153
A priori structure      148
Admissibility criteria      138
Affinity function      132
Agglomerative hierarchical clustering      59
Agglomerative scheme      72
Agrawala, A. K.      224
Ahuja, N.      125 128
Alagar, V. S.      44 213
Algorithm for bootstrap parameter estimation      160
Algorithm for generating clustered data      274
Algorithm, agglomerative      57
Algorithm, agglomerative, complete-link clustering      61
Algorithm, agglomerative, single-link clustering      61
Algorithm, baseline distribution, of $\gamma$ under random graph hypothesis      168
Algorithm, baseline distribution, of external index for partitional adequacy      176
Algorithm, baseline distribution, of internal index      165
Algorithm, complete-link method      63
Algorithm, complete-link, Johnson’s      72
Algorithm, divisive      57
Algorithm, divisive, single-link      70
Algorithm, fuzzy partitional clustering      133
Algorithm, iterative, partitional clustering      96
Algorithm, Monte Carlo estimation by binomial sampling      156
Algorithm, MST-based test of clustering tendency      214
Algorithm, multidimensional scaling      47
Algorithm, mutual neighborhood clustering      129
Algorithm, nearest neighbor clustering      128
Algorithm, Neyman - Scott process      208
Algorithm, options, hierarchical clustering      57
Algorithm, Prim’s for MST      271
Algorithm, projection      25
Algorithm, projection pursuit      42
Algorithm, relation to clustering method      58
Algorithm, SAHN      57
Algorithm, single-link clustering from graph theory      70
Algorithm, single-link method      63
Algorithm, single-link, Johnson’s      72
Algorithm, Zahn’s clustering      121
ALSCAL      53
Alternative hypothesis, aggregation      203 207
Alternative hypothesis, aggregation, regularity      208
Analysis of variance      179
Anderberg, M. R.      5 12 14 16 17 74 86 96 97 134 138
Ando      37
Andrews plots      136
Andrews representation      38
Andrews, D. F.      38 228
Andrews, H. C.      45
Arabie, P.      56 172 175
Arbitrary taxonomy      170
Archetypes      39
artificial intelligence      224
Atkinson, A. C.      159
Average distance      20
Average error      178
Average square-error      33
Average-link clustering, comparative analysis      138
Backer, E.      132
Bailey, D. E.      5
Bailey. T. A.      189 195 198
Baker, F. B.      72 141 167 170 193 221
Ball, G. H.      98 247 254
Ballard, D. H.      224 227 232 238 240
Barrow, H. G.      232
Bartels, P. H.      17
Barton, D. E.      148
Barycentric transformation      45
Basal taxonomies      170
Baseline distribution for external indices of partitional adequacy      175
Baseline distribution for Fowlkes and Mallows statistic      175
Baseline distribution for Hubert’s $\Gamma$ statistic, example      163
Baseline distribution for index of cluster validity      161
Baseline distribution for internal and external indices      164
Baseline distribution for Jaccard coefficient      21
Baseline distribution for permutation statistic      23
Baseline distribution for simple matching coefficient      21
Baseline distribution for square-error      178
Baseline distribution, for CPCC      167
Baseline distribution, in Monte Carlo analysis      158
Baseline distribution, in test for nonrandom structure      162
Baseline population, in cluster validity      161
Baseline population, in cluster validity with random graph hypothesis      145
Baseline population, in cluster validity with random label hypothesis      145
Baseline population, in cluster validity, in testing hypotheses      144
Basis vector      32
Basis vector, Try clustering      134
Basu, J. P.      117
Bayne, N. K.      139
Bellman, R. E.      131
Bennett, R. S.      44
Bentler, P.      53
Besag, J. E.      158
Best-case compactness index      194
Best-case isolation index      194
Best-case method      192
Between-cluster scatter matrix      94
Between-group scatter matrix      259
Between-group similarities      4
Bezdek, J.      131 132
Bias in decision rules, bootstrapping      160
Bickel, P. J.      160
Binary features      12
Binary proximity index      12
Binary relation      60
Binary relation from threshold graph      60
Binary taxonomy      170
Binder, D. A.      118
Binomial coefficient      21 250
Binomial distribution      157
Binomial distribution, Monte Carlo sampling      156
Binomial sampling in Monte Carlo analysis      155 157
Binomial sampling m Poisson process      203
Biswas, G.      40 41
Blashfield, R. K.      134 139
Blashfield, R. K.,      140
Blurring of critical region      158
Bock, H. H.      179 182
Bootstrapping      159-161
Botryology      4
Bouldin, D. W.      185
Boundaries, fuzzy      131
Box test      264
Brockett, P. L.      20
Brown C. M.      224 227 232 238 240
Brown T.      214
Bryant, J.      235
Buffer zone      220
Busk, P.      53
Caelli      53
Calhoun distance      17
Calvert, O. W.      37 42
Carlton, S.      228
Carmichael, J. W.      24
Carmone, F. J.      47
Carroll, J. D.      42 47 52 252
Categorical information      8
Category information, and linear projections      26
Category information, and nonlinear projections      37
Category information, with external criteria      161
Category labels and baseline distribution of external index      176
Category labels and cluster analysis      1
Category labels and discriminant analysis      26 35
Category labels and external indices of partitional structure      172
Category labels and Hubert’s $\Gamma$ statistic      148 151
Category labels and internal index of partitional structure      177
Category labels and intrinsic classification      56
Category labels, DATA      1 30
Category labels, in FORGY      102
Cavalli - Sforza, L. L.      91
Central limit theorem      157
Centroid method      80
Centroid method, non monotone property      85
Centroid of cluster      94
Chained structures      129
Chained taxonomy      170
Chaining, singie-link dendrogram      75
Chandrasekaran, B.      243
Chang, C. L.      40
Chang, W. C.      29
Channel-filtering model      227
Characteristic function for partition      18
Chebychev inequality      157
Chemick, M. R.      160
Chemoff, H.      37 38
Chen, H. J.      223
Chen, N. K.      45
Cheng, Y.      224
Chernoff faces      37 136
Chhikara, R. S.      119
Chi-square statistic      213
Chien, Y.      38
Cigar-shaped data      108
City block distance      15
Clark, P.      217
Classification      55
Clifford, H.      5 57
CLIQUE      270
Clique and complete-link clustering      61 63 65
CLUSTAN      134
Cluster analysis, definition      1
Cluster analysis, difference from pattern recognition      241
Cluster analysis, packages      134
Cluster applications      223-240
Cluster axiomatic basis      138
Cluster center      93
Cluster label and clustering methods      14
Cluster label and F-distribution      103
Cluster label and intrinsic classification      57
Cluster label with SOX data      163-64
Cluster membership function      132
Cluster profile, as plots      195
Cluster profile, definition      191
Cluster profile, examples      191 92
CLUSTER program and DATA      2 111
CLUSTER program, DATA      1
CLUSTER program, description      108
CLUSTER program, effect of dimensionality      178
CLUSTER program, effect of number of clusters      178
CLUSTER program, in range image segmentation      233
Cluster shape      91
Cluster shape, prior knowledge      123
Cluster validation      143
Cluster validity      143-222
Cluster validity of individual clusters      188-201
Cluster validity, in hierarchical structures      165-72
Cluster validity, in image segmentation      228
Cluster validity, in partitional structures      172-88
Cluster validity, indices      160
Cluster validity, statistical tools for      161
Cluster, (k,r)      88
Cluster, as collection of patterns      8
Cluster, cigar-shaped      29
Cluster, crisp      131
Cluster, definitions      1
Cluster, description      161
Cluster, fuzzy      131
Cluster, lifetime      197 198
Cluster, line-like      122
Cluster, non-homogeneous      122
Cluster, touching      122
Cluster-by-category table, eightyX data      162
Clustering algorithm and clustering method      96 134
Clustering and decision making      4
Clustering criterion, determinant of scatter matrix      95
Clustering criterion, from scatter matrices      95
Clustering criterion, global      90
Clustering criterion, in partitional clustering      90
Clustering criterion, local      90
Clustering method      55-142
Clustering method and clustering algorithm      96
Clustering method for image registration      240
Clustering method, difference from clustering algorithm      134
Clustering method, square-error      96
Clustering method, to group pixeis      226
Clustering methodology      135 41
Clustering of pixels in spectral domain      235
Clustering packages      133
Clustering software      133-35
Clustering strategy      137
Clustering structure and a priori structure      148
Clustering structure, adequacy of      160
Clustering structure, criteria for      161
Clustering structure, unusualness of      143
Clustering structure, validation by bootstrapping      159
Clustering structure, validity of      146
Clustering tendency      201-222
Clustering tendency, in clustering methodology      136
Clustering, by cutting a dendrogram      59
Clustering, by density estimation      118
Clustering, by graph theory      120
Clustering, by mixture decomposition      117
Clustering, conceptual      92
Clustering, hierarchical      58-89
Clustering, nearest neighbor      128
Clustering, partitional      89-133
Clustering, space-time      148
CM-reachabic method      192
Coggins, J.      227 228 231
Cohen, A.      223
Coleman, G. B.      228
Communality      261
Compact cluster      192
Compactness index of cluster, best-case      194
Compactness index of cluster, best-case, external      191
Compactness index of cluster, best-case, internal      192
Compactness of cluster and within-ciuster scatter matrix      95
Compactness of cluster, definition      189
Compactness of cluster, in duster vafidity      188-89
Comparative analysis      137
Comparison of algorithms in image segmentation      233
Complete graph      269
Complete spatial randomness      201
Complete-link algorithm, agglomerative      61
Complete-link algorithm, by matrix updating      79
Complete-link algorithm, from graph theory      60
Complete-link algorithm, Hubert’s      63
Complete-link algorithm, Johnson’s      72
Complete-link cluster, as clique      64
Complete-link cluster, visual assessment      75
Complete-link clustering and complete subgraphs      62
Complete-link clustering, comparative analysis      138
Complete-link clustering, in range image segmentation      233
Complete-link hierarchy and relative criteria      161
Complete-link method, characterization      65
Complete-link method, difference from single-link      74
Complete-link method, graph theory algorithms      70
Complete-link method, matrix updating      72
Complexity of K-means algorithms      100
Component densities      117
Computational complexity      3
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