Clustering ensemble algorithm for categorical data
一种面向分类属性数据的聚类融合算法研究*

In order to prevent the inaccuracy and randomness of single clustering algorithm, and error of existing clustering algorithm transferring categorical data into numerical data for clustering, this paper proposed the clustering ensemble for categorical data. The algorithm produced clustering memberships by values of categorical data, and then used similarity degree to partition dataset, which reduced the process of clustering by minimizing the objective function. Finally, applied the algorithm into UCI dataset. The results show its efficiency and accuracy are better than existing algorithms, the design and refreshing methods are effective.