Authors: Alguwaizani, Abdulrahman
Hansen, Pierre
Mladenović, Nenad 
Ngai, Eric
Affiliations: Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Variable neighborhood search for harmonic means clustering
Journal: Applied Mathematical Modelling
Volume: 35
Issue: 6
First page: 2688
Last page: 2694
Issue Date: 1-Jan-2011
Rank: M21
ISSN: 0307-904X
DOI: 10.1016/j.apm.2010.11.032
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.
Keywords: Clustering | K-harmonic means | Metaheuristics | Minimum sum of squares | Unsupervised learning | Variable neighborhood search
Publisher: Elsevier

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