一种新的变长染色体表示遗传聚类算法

Ming-an Zhang, Yong Deng, Dong-xia Chang
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引用次数: 2

摘要

本文提出了一种新的变长染色体表示遗传聚类算法(GCVCR),该算法能够自动进化并找到数据集的最优聚类数和合适的聚类中心。为了提高聚类问题的有效性,提出了一种新的基于数据点之间的信息传递和染色体描述的候选中心的聚类准则。仿真结果表明了该算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel genetic clustering algorithm with variable-length chromosome representation
The paper proposed a new genetic clustering algorithm with variable-length chromosome representation (GCVCR), which can automatically evolve and find the optimal number of clusters as well as proper cluster centers of the data set. A new clustering criterion based on message passing between data points and the candidate centers described by the chromosome are presented to make the clustering problem more effective. The simulation results show the effectiveness of the proposed algorithm.
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