A Prototypes-Embedded Genetic K-means Algorithm

Authors:
Shih-Sian Cheng;Yi-Hsiang Chao;Hsin-Min Wang;Hsin-Chia Fu
Affiliations:
National Chiao Tung University, Hsinchu, Taiwan;National Chiao Tung University, Hsinchu, Taiwan;Institute of Information Science, Academia Sinica, Taipei, Taiwan;National Chiao Tung University, Hsinchu, Taiwan
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Year:
2006

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Abstract

This paper presents a genetic algorithm (GA) for Kmeans clustering. Instead of the widely applied stringof- group-numbers encoding, we encode the prototypes of the clusters into the chromosomes. The crossover operator is designed to exchange prototypes between two chromosomes. The one-step K-means algorithm is used as the mutation operator. Hence, the proposed GA is called the prototypes-embedded genetic K-means algorithm (PGKA). With the inherent evolution process of evolutionary algorithms, PGKA has superior performance than the classical K-means algorithm, while comparing to other GA-based approaches, PGKA is more efficient and suitable for large scale data sets.