A clustering method combining differential evolution with the K-means algorithm

作者：Kwedlo Wojciech^*

来源：Pattern Recognition Letters, 2011, 32(12): 1613-1621.

DOI：10.1016/j.patrec.2011.05.010

• Cluster analysis
• Differential evolution
• K-means algorithm

The present paper considers the problem of partitioning a dataset into a known number of clusters using the sum of squared errors criterion (SSE). A new clustering method, called DE-KM, which combines differential evolution algorithm (DE) with the well known K-means procedure is described. In the method, the K-means algorithm is used to fine-tune each candidate solution obtained by mutation and crossover operators of DE. Additionally, a reordering procedure which allows the evolutionary algorithm to tackle the redundant representation problem is proposed. The performance of the DE-KM clustering method is compared to the performance of differential evolution, global K-means method, genetic K-means algorithm and two variants of the K-means algorithm. The experimental results show that if the number of clusters K is sufficiently large, DE-KM obtains solutions with lower SSE values than the other five algorithms.

• 出版日期2011-9-1