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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
Yu, Clement T. – Information Storage and Retrieval, 1974
Heuristic methods for the construction of term classes are presented and experimental results are obtained to illustrate the usefulness of the method. (Author/PF)
Descriptors: Algorithms, Automatic Indexing, Classification, Cluster Grouping
Peer reviewed Peer reviewed
McQuitty, Louis L.; Koch, Valerie L. – Educational and Psychological Measurement, 1975
Develops and illustrates a method for clustering hierarchically the interrelationships between many persons, as represented in a matrix of a thousand by a thousand. (RC)
Descriptors: Classification, Cluster Grouping, Matrices, Measurement Techniques
Hubert, Lawrence; Schultz, James – 1975
An empirical assesssment of the space distortion properties of two prototypic hierarchical clustering procedures is given in terms of an occupancy model developed from combinatorics. Using one simple example, the single-link and complete-link clustering strategies now in common use in the behavioral sciences are empirically shown to be space…
Descriptors: Behavioral Sciences, Classification, Cluster Analysis, Cluster Grouping
Peer reviewed Peer reviewed
Harding, Alan F.; Willett, Peter – Journal of the American Society for Information Science, 1980
Demonstrates that the process of comparing each document in an automated system with all others during the classification procedure may be avoided by the use of an inverted file. (FM)
Descriptors: Automatic Indexing, Classification, Cluster Grouping, Information Retrieval
Peer reviewed Peer reviewed
Peay, Edmund R. – Psychometrika, 1975
A class of closely related hierarchical grouping methods are discussed and a procedure which implements them in an integrated fashion is presented. These methods avoid some theoretical anomalies inherent in clustering and provide a framework for viewing partitioning and nonpartitioning grouping. Significant relationships between these methods and…
Descriptors: Classification, Cluster Grouping, Computer Programs, Data Analysis