A sequential ensemble clusterings generation algorithm for mixed data.

Ensemble clustering has attracted much attention for its robustness, stability, and accuracy in academic and industry communities. In order to yield base clusterings with high quality and diversity simultaneously in ensemble clustering, many efforts have been done by exploiting different clustering models and data information. However, these methods neglect correlation between different base clusterings during the process of base clusterings generation, which is important to obtain a quality and diverse clustering decision. To overcome this deficiency, a sequential ensemble clusterings generation algorithm for mixed data is developed in this paper based on information entropy. The first high quality base clustering is yield by maximizing the entropy-based criterion. Afterward, a sequential paradigm is utilized to incrementally find more base clusterings, in which the diversity between a new base clustering and the former base partitions is measured by the normalized mutual information. Extensive experiments conducted on various data sets have demonstrated the superiority of our proposal as compared to several existing base clusterings generation algorithms.