Authors: Costa, Leandro
Aloise, Daniel
Mladenović, Nenad 
Title: Less is more: basic variable neighborhood search heuristic for balanced minimum sum-of-squares clustering
Journal: Information Sciences
Volume: 415-416
First page: 247
Last page: 253
Issue Date: 1-Nov-2017
Rank: M21a
ISSN: 0020-0255
DOI: 10.1016/j.ins.2017.06.019
Abstract: 
Clustering addresses the problem of finding homogeneous and well-separated subsets, called clusters, from a set of given data points. In addition to the points themselves, in many applications, there may exist constraints regarding the size of the clusters to be found. Particularly in balanced clustering, these constraints impose that the entities be equally spread among the different clusters. In this work, we present a basic variable neighborhood search heuristic for balanced minimum sum-of-squares clustering, following the recently proposed “Less Is More Approach”. Computational experiments and statistical tests show that the proposed algorithm outperforms the current state-of-the-art algorithm for the problem, indicating that non sophisticated and easy to implement metaheuristic methods can be sufficient to produce successful results in practice.
Keywords: Balanced clustering | Minimum sum-of-squares | Optimization
Publisher: Elsevier
Project: CNPq-Brazil grants 308887/2014-0 and 400350/2014-9
RSF grant 14-41-00039

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