Correlation clustering based on genetic algorithm for documents clustering
Correlation clustering based on genetic algorithm for documents clustering
复制标题
基于遗传算法的相关聚类文档聚类
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Qiansheng Fang
中科院分区：
文献类型：
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作者：
Zhenya Zhang;Hongmei Cheng;Wanli Chen;Shuguan Zhang;Qiansheng Fang
相关聚类问题是一个NP难题，相关聚类问题的解决技术可以利用给定数据集中数据的关系矩阵对给定数据集进行聚类。本文提出了一种基于遗传算法的相关聚类问题的方法，称为GeneticCC。为了评估聚类划分的性能，本文定义了基于数据相关性的聚类精度，并讨论了聚类精度的特征。实验结果表明，以聚类精度为标准，GeneticCC对UCI文档数据集构建的聚类划分性能优于SOM神经网络构建的其他聚类划分的聚类性能。
Correlation clustering problem is a NP hard problem and technologies for the solving of correlation clustering problem can be used to cluster given data set with relation matrix for data in the given data set. In this paper, an approach based on genetic algorithm for correlation clustering problem, named as GeneticCC, is presented. To estimate the performance of a clustering division, data correlation based clustering precision is defined and features of clustering precision are discussed in this paper. Experimental results show that the performance of clustering division for UCI document data set constructed by GeneticCC is better than clustering performance of other clustering divisions constructed by SOM neural network with clustering precision as criterion.