The modified kernel clustering algorithm is faster than the classical algorithm in convergence and more accurate in clustering.

该聚类方法在性能上比经典聚类算法有所改进，具有更快的收敛速度以及较高的准确度。

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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。

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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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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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