An improved link analysis based clustering ensemble method

Li-Juan Wang, Z. Hao
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Abstract

This paper proposes an improved link analysis based clustering ensemble method (ILCEM). ILCEM can transform binary data-cluster association matrix into real-valued matrix according to the similarity between clusters in all base clustering. The refined data-cluster association matrix can generate more information to clustering ensemble so as to improve the performance of clustering. Experimental results on three VCI datasets have shown that ILCEM is better than KMC, base clustering method and CSM+GKMC.
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一种改进的基于链接分析的聚类集成方法
提出了一种改进的基于链接分析的聚类集成方法。ILCEM可以根据所有基聚类中聚类之间的相似度将二值数据-聚类关联矩阵转化为实值矩阵。改进后的数据-聚类关联矩阵可以为聚类集成生成更多的信息,从而提高聚类性能。在三个VCI数据集上的实验结果表明,ILCEM方法优于KMC方法、基聚类方法和CSM+GKMC方法。
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