Abstract:Mixed attributes data clustering is a research hotspot in recent years. For mixed attributes data clustering algorithm, it requires handling numeric attributes and categorical attributes simultaneously. However many algorithms have not very good balance with numeric and categorical attributes, and the cluster results are not satisfied. For mixed attributes data set, a new clustering ensemble algorithm based on intersection is proposed. It processes the numeric attributes with a new relative density clustering algorithm, and processes the categorical attributes with a clustering algorithm based on information entropy. Then it fuses these two cluster members with a cluster fusion algorithm based on intersection. Finally, it gets the clustering results. It is validated by taking an experiment on UCI data set Zoo, and compared with the existing k-prototypes algorithm and EM algorithm. The experiment result shows that the new algorithm has higher flexibility and accuracy. The influence of the intersection element ratioand to the result is also discussed.