Abstract
Given a finite setE ⊂R n, the problem is to find clusters (or subsets of “similar” points inE) and at the same time to find the most typical elements of this set. An original mathematical formulation is given to the problem. The proposed algorithm operates on groups of points, called “samplings” (“samplings” may be called “multiple centers” or “cores”); these “samplings” adapt and evolve into interesting clusters. Compared with other clustering algorithms, this algorithm requires less machine time and storage. We provide some propositions about nonprobabilistic convergence and a sufficient condition which ensures the decrease of the criterion. Some computational experiments are presented.
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Diday, E. The dynamic clusters method in nonhierarchical clustering. International Journal of Computer and Information Sciences 2, 61–88 (1973). https://doi.org/10.1007/BF00987153
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DOI: https://doi.org/10.1007/BF00987153