A Prototypes-Embedded Genetic K-means Algorithm

This paper presents a genetic algorithm (GA) for K-means clustering. Instead of the widely applied string-of-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...

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Bibliographic Details
Main Authors: Shih-Sian Cheng, Yi-Hsiang Chao, Hsin-Min Wang, Hsin-Chia Fu
Format: Conference Proceeding
Language:English
Subjects:
Biological cells
Chaos
Clustering algorithms
Encoding
Genetic algorithms
Genetic mutations
Large-scale systems
Partitioning algorithms
Pattern recognition
Prototypes
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Summary:This paper presents a genetic algorithm (GA) for K-means clustering. Instead of the widely applied string-of-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
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2006.155