Clustering Categorical Data Based on Maximal Frequent Itemsets
Dadong Yu, Dongbo Liu, Rui Luo, Jianxin Wang · 2007
Clustering categorical data received more attention since recent years, but several aspects of the existing algorithms, such as the interpretabilities of found clusters, the impact of data selection orders, are not well solved. A novel categorical data clustering algorithm called CLUBMIS is proposed in this paper, which can effectively find the interesting clusters. In addition, the clusters can be easily interpreted by the maximal frequent itemsets used in the clustering process. Different from most of the hierarchical clustering algorithm, CLUBMIS clusters datasets based on the summarized information, i.e. maximal frequent itemsets, thus it eliminates the effect of different data selection order.