基于最大联盟粗糙集的三支聚类.

In order to address the limitations of the neighborhood rough set model caused by neighborhood parameters and inadequate sample information representation, this paper proposed a rough set model based on the maximum coalition theory. By normalizing the neighborhood information system and utilizing the maximum coalition set to describe neighborhood granular information, the proposed model enabled finer information division and significant reduction of uncertainty in the boundary region. Combining the model with three-way clustering, this paper designed a three-way clustering algorithm based on maximum coalition rough set. Comparative experiments on six UCI public data sets show that the proposed algorithm has better clustering quality than the comparison algorithms, and has higher comparison accuracy when dealing with boundary region samples. [ABSTRACT FROM AUTHOR]