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Genetic algorithm-based clustering technique
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DOI：10.1016/S0031-3203(99)00137-5.png)
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Abstract
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A genetic algorithm-based clustering technique, called GA-clustering, is proposed in this article. The searching capability of genetic algorithms is exploited in order to search for appropriate cluster centres in the feature space such that a similarity metric of the resulting clusters is optimized. The chromosomes, which are represented as strings of real numbers, encode the centres of a fixed number of clusters. The superiority of the GA-clustering algorithm over the commonly used K-means algorithm is extensively demonstrated for four artificial and three real-life data sets. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keywords:
genetic algorithms
clustering metric
K-means algorithm
real encoding
Euclidean distance
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