基于随机取样的选择性K-means聚类融合算法

Selective K-means clustering ensemble based on random sampling

由于缺少数据分布、参数和数据类别标记的先验信息,部分基聚类的正确性无法保证,进而影响聚类融合的性能;而且不同基聚类决策对于聚类融合的贡献程度不同,同等对待基聚类决策,将影响聚类融合结果的提升。为解决此问题,提出了基于随机取样的选择性K-means聚类融合算法(RS-KMCE)。该算法中的随机取样策略可以避免基聚类决策选取陷入局部极小,而且依据多样性和正确性定义的综合评价值,有利于算法快速收敛到较优的基聚类子集,提升融合性能。通过2个仿真数据库和4个UCI数据库的实验结果显示:RS-KMCE的聚类性能优于K-means算法、K-means融合算法(KMCE)以及基于Bagging的选择性K-means聚类融合(BA-KMCE)。

Without any prior information about data distribution, parameter and the labels of data, not all base clustering results can truly benefit for the combination decision of clustering ensemble. In addition, if each base clustering plays the same role, the performance of clustering ensemble may be weakened. This paper proposed a selective K-means clustering ensemble based on random sampling, called RS-KMCE. In RS-MKCE, random sampling can avoid local minimum in the process of selecting base clustering subset for ensemble. And the defined evaluation index according to diversity and accuracy can lead to a better base clustering subset for improving the performance of clustering ensemble. The experiment results on two synthetic datasets and four UCI datasets show that performance of the proposed RS-KMCE is better than K-means, K-means clustering ensemble, and selective K-means clustering ensemble based on bagging.