期刊
APPLIED MATHEMATICAL MODELLING
卷 35, 期 6, 页码 2688-2694出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2010.11.032
关键词
Clustering; Unsupervised learning; Minimum sum of squares; K-harmonic means; Metaheuristics; Variable neighborhood search
Harmonic means clustering is a variant of minimum sum of squares clustering (which is sometimes called K-means clustering), designed to alleviate the dependance of the results on the choice of the initial solution. In the harmonic means clustering problem, the sum of harmonic averages of the distances from the data points to all cluster centroids is minimized. In this paper, we propose a variable neighborhood search heuristic for solving it. This heuristic has been tested on numerous datasets from the literature. It appears that our results compare favorably with recent ones from tabu search and simulated annealing heuristics. (C) 2010 Elsevier Inc. All rights reserved.
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