G-2009-44
Variable Neighbourhood Search for Harmonic Means Clustering
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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 results, obtained on standard test instances, compare favorably with recent ones from Tabu search and Simulated annealing heuristics.
Paru en septembre 2009 , 15 pages
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jan. 2011
Variable neighborhood search for harmonic means clustering
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Applied Mathematical Modelling, 35(6), 2688–2694, 2011
référence BibTeX