A Prototypes-Embedded Genetic K-means Algorithm

Shih-Sian Cheng Shih-Sian Cheng,Yi-Hsiang Chao Yi-Hsiang Chao,Hsin-Chia Fu Hsin-Chia Fu,Hsin-Min Wang Hsin-Min Wang

doi:10.1109/icpr.2006.155

A Prototypes-Embedded Genetic K-means Algorithm

Shih-Sian Cheng Shih-Sian Cheng, Yi-Hsiang Chao Yi-Hsiang Chao + Show 2 more

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https://doi.org/10.1109/icpr.2006.155

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Publication Date: Jan 1, 2006

Citations: 18

Affiliation: National Yang Ming Chiao Tung University, Institute of Information Science, Academia Sinica

#Genetic Algorithm #Classical K-means Algorithm + Show 8 more

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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.

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