A Prototypes-Embedded Genetic K-means Algorithm

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.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Hsin-Chia Fu, Hsin-Min Wang, Shih-Sian Cheng, Yi-Hsiang Chao
• Keywords: Computer Vision, Genetic Algorithm, ICPR 2006, One-step K-means, Prototypes-embedded Genetic K-means