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Peer reviewed Peer reviewed
Church, Marjorie; And Others – Journal of Educational and Psychological Measurement, 1974
Descriptors: Cluster Analysis, Cluster Grouping, Computer Programs
Peer reviewed Peer reviewed
Milligan, Glenn W. – Multivariate Behavioral Research, 1989
Simulated test data (N=864 artificial data sets) with four different error conditions were used to study the recovery characteristics of the beta-flexible clustering method. Conditions under which the beta-flexible method provides good recovery are discussed. (SLD)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Simulation
Peer reviewed Peer reviewed
Gold, E. Mark; Hoffman, Paul J. – Multivariate Behavioral Research, 1976
A clustering technique is described, the objective of which is to detect deviant subpopulations which deviate from a primary subpopulation in a well defined direction. (Author/DEP)
Descriptors: Algorithms, Cluster Analysis, Cluster Grouping, Mathematical Models
Peer reviewed Peer reviewed
Tzeng, Oliver C. S.; May, William H. – Educational and Psychological Measurement, 1979
A strategy for reordering the hierarchical tree structure is presented. While the order of terminal nodes of Johnson's procedure is arbitrary, this procedure will rearrange every triad of nodes under a common least upper node so that the middle node is nonarbitrarily closest to the anchored node. (Author/CTM)
Descriptors: Cluster Analysis, Cluster Grouping, Matrices, Multidimensional Scaling
Peer reviewed Peer reviewed
Peay, Edmund R. – Psychometrika, 1975
Peay presented a class of grouping methods based on the concept of the r-clique for symmetric data relationships. The concepts of the r-clique can be generalized readily to directed (or asymmetric) relationships, and groupings based on this generalization may be found conveniently using an adoption of Peay's methodology. (Author/BJG)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Mathematical Models
Peer reviewed Peer reviewed
Hubert, Lawrence J. – Psychometrika, 1974
The connection between graph theory and clustering is reviewed and extended. Major emphasis is on restating, in a graph-theoretic context, selected past work in clustering, and conversely, developing alternative strategies from several standard concepts used in graph theory per se. (Author/RC)
Descriptors: Cluster Analysis, Cluster Grouping, Literature Reviews, Statistical Analysis
Peer reviewed Peer reviewed
Yu, Clement T. – Journal of the American Society for Information Science, 1974
A clustering algorithm which is tree-like in structure, and is based on user queries, is presented. It is compared to some existing algorithms and is found to be superior. (Author)
Descriptors: Algorithms, Classification, Cluster Analysis, Cluster Grouping
Peer reviewed Peer reviewed
Milligan, Glenn W.; Cooper, Martha C. – Multivariate Behavioral Research, 1986
Five external criteria were used to evaluate the extent of recovery of the true structure in a hierarchical clustering solution. The results of the study indicated that the Hubert and Arabie adjusted Rank index was best suited to the task of comparison across hierarchy levels. (Author/LMO)
Descriptors: Cluster Analysis, Cluster Grouping, Measurement Techniques, Statistical Studies
Peer reviewed Peer reviewed
Spreen, Otfried; Haaf, Robert G. – Journal of Learning Disabilities, 1986
Test scores of two groups of learning disabled children (N=63 and N=96) were submitted to cluster analysis in an attempt to replicate previously described subtypes. All three subtypes (visuo-perceptual, linguistic, and articulo-graphomotor types) were identified along with minimally and severely impaired subtypes. Similar clusters in the same…
Descriptors: Cluster Analysis, Cluster Grouping, Learning Disabilities, Longitudinal Studies
Peer reviewed Peer reviewed
Overall, John E.; Free, Spencer M. – Journal of Educational and Psychological Measurement, 1974
Descriptors: Cluster Analysis, Cluster Grouping, Computer Programs, Multidimensional Scaling
Peer reviewed Peer reviewed
Ritchie, J. R. Brent – Journal of Leisure Research, 1975
This paper describes an attempt to derive an empiracally based method for the classification of leisure activities. (RC)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Leisure Time
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Halff, Henry M. – 1975
Graphical methods for evaluating the fit of Johnson's hierarchical clustering schemes are presented together with an example. These evaluation methods examine the extent to which the clustering algorithm can minimize the overlap of the distributions of intracluster and intercluster distances. (Author)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Graphs
Peer reviewed Peer reviewed
Murtagh, F. – Information Processing and Management, 1984
Using examples of data from the areas of information retrieval and of multivariate data analysis, six hierarchic clustering algorithms (single link, median, centroid, group average, complete link, Wards's) are examined and evaluated by using three proposed coefficients of hierarchic structure. Nine references are cited. (EJS)
Descriptors: Algorithms, Cluster Analysis, Cluster Grouping, Data Analysis
Peer reviewed Peer reviewed
Yu, C. T. – Journal of the American Society for Information Science, 1976
A measure for the quantification of the changes in classification under small changes in data is proposed. (Author)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Information Retrieval
Peer reviewed Peer reviewed
Guertin, Wilson H. – Educational and Psychological Measurement, 1971
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Factor Analysis
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