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A versatile clustering algorithm with objective function and objective measure

✍ Scribed by Judith M.S. Prewitt

Publisher
Elsevier Science
Year
1972
Weight
1022 KB
Volume
2
Category
Article
ISSN
0010-468X

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✦ Synopsis

A computer program for nonparametric cluster synthesis, using similarity rather than maximum likelihood as the basis for class membership, is presented. The algorithm utilizes recursive computations to develop a hierarchy or tree of nested clusters. The major components of the program are: (1) a (dis)similarity function. (2) a grouping or merger strategy, based on optimizing a dynamic objective function, and (3) a halting criterion, based on evaluating a dynamic objective measure. Program options permit variations of data normalization, measures of similarity, and clustering strategy. A variety of hard-copy summaries and displays are available to the user. An illustrative application to the classification of human mitotic chromosomes is included.

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A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering
✍ Hyun-Sook Rhee; Kyung-Whan Oh πŸ“‚ Article πŸ“… 1996 πŸ› Springer US 🌐 English βš– 643 KB

Fuzzy clustering has played an important role in solving many problems. In this paper, we design an unsupervised neural network model based on a fuzzy objective function, called OFUNN. The learning rule for the OFUNN model is a result of the formal derivation by the gradient descent method of a fuzz