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A clustering method combining differential evolution with the K-means algorithm
- Source :
-
Pattern Recognition Letters . Sep2011, Vol. 32 Issue 12, p1613-1621. 9p. - Publication Year :
- 2011
-
Abstract
- Abstract: 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 32
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 62267729
- Full Text :
- https://doi.org/10.1016/j.patrec.2011.05.010