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

作者：Arellano Verdejo Javier^*; Alba Enrique; Godoy Calderon Salvador

来源：Soft Computing, 2016, 20(3): 895-905.

DOI：10.1007/s00500-014-1548-6

• Partitional clustering
• Optimum number of clusters
• Evolutionary algorithms
• Differential evolution
• Local search

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 fewmethods existing in the literature always use some sort of evolutionary algorithm having a cluster validation index as its objective function. In this article, a newevolutionary 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.

• 出版日期2016-3