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Soft Computing

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14.12.2014 | Methodologies and Application

Efficiently finding the optimum number of clusters in a dataset with a new hybrid differential evolution algorithm: DELA

verfasst von: Javier Arellano-Verdejo, Enrique Alba, Salvador Godoy-Calderon

Erschienen in: Soft Computing | Ausgabe 3/2016

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Abstract

Clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. The few methods existing in the literature always use some sort of evolutionary algorithm having a cluster validation index as its objective function. In this article, a new evolutionary algorithm, based on a hybrid model of global and local heuristic search, is proposed for the same task, and some experimentation is done with different datasets and indexes. Due to its design, independent of any clustering procedure, it is applicable to virtually any clustering method like the widely used \(k\)-means algorithm. Moreover, the use of non-parametric statistical tests over the experimental results, clearly show the proposed algorithm to be more efficient than other evolutionary algorithms currently used for the same task.

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Titel: Efficiently finding the optimum number of clusters in a dataset with a new hybrid differential evolution algorithm: DELA
verfasst von: Javier Arellano-Verdejo
Enrique Alba
Salvador Godoy-Calderon
Publikationsdatum: 14.12.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 3/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1548-6

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