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Hubert, Lawrence – Psychometrika, 1973
The present paper discusses two rather different types of partitioning techniques that still have the same property of monotone invariance. (Author)
Descriptors: Algorithms, Cluster Analysis, Cluster Grouping, Goodness of Fit
Peer reviewed Peer reviewed
Hubert, Lawrence – Psychometrika, 1972
Paper is an attempt to extend the hierarchical partitioning algorithms and to emphasize a general connection between these clustering procedures and the mathematical theory of lattices. (Author)
Descriptors: Algorithms, Cluster Grouping, Goodness of Fit, Mathematical Applications
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White, Lee J.; And Others – 1975
The major advantage of sequential classification, a technique for automatically classifying documents into previously selected categories, is that the entire document need not be processed before it is classified. This method assumes the availability of a priori categories, a selection of keywords representative of these categories, and the a…
Descriptors: Algorithms, Automatic Indexing, Bayesian Statistics, Classification
Kar, B. Gautam; White, Lee J. – 1975
The feasibility of using a distance measure, called the Bayesian distance, for automatic sequential document classification was studied. Results indicate that, by observing the variation of this distance measure as keywords are extracted sequentially from a document, the occurrence of noisy keywords may be detected. This property of the distance…
Descriptors: Algorithms, Automatic Indexing, Bayesian Statistics, Classification
McRae, Douglas J. – 1971
Procedures for grouping students into homogeneous subsets have long interested educational researchers. The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as criteria for adequate grouping are given and…
Descriptors: Ability Grouping, Algorithms, Cluster Grouping, Criterion Referenced Tests