By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed for clustering analysis.
通过研究核聚类算法,以及粗糙集,提出了一个新的用于聚类分析的粗糙核聚类方法。
The experimental results show that the proposed algorithm is superior to the improved kernel clustering algorithm and K-means in good astringency, stability and overall optimal solutions.
实验结果表明,使用该算法的聚类比改进的核聚算法、K均值算法等单一方法具有良好的收敛性、稳定性和更高的全局最优。
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