Genetic algorithm-based clustering technique
Genetic algorithm-based clustering technique
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DOI：
10.1016/s0031-3203(99)00137-5
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发表时间：
2000-09-01
影响因子：
8
通讯作者：
Bandyopadhyay, S
中科院分区：
文献类型：
--
作者：
Maulik, U;Bandyopadhyay, S
本文提出了一种基于遗传算法的聚类技术，称为GA聚类。遗传算法的搜索能力被利用，以便在特征空间中搜索适当的聚类中心，从而优化所得到的聚类的相似性度量。染色体被表示为一串真实的数字，编码固定数目的簇的中心。GA聚类算法的优越性，常用的K-均值算法被广泛证明为四个人工和三个现实生活中的数据集。(C)2000模式识别学会。由Elsevier Science Ltd.出版，版权所有。
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.