An enhanced cluster validity index method comprising Rough Set theory and modified PBMF index function

This study proposes a method for partitioning and classifying complex datasets based on the Rough Set (RS) theory and a modified form of the PBMF-index method. In contrast to the traditional PBMF-index method, the proposed approach, designated as the Huang-index method, partitions the attributes rather than the data and optimizes both the number of clusters and classification accuracy. Overall, the results show that the Huang-index method not only has a better clustering performance than the PBMF-index method, but also achieves a greater classification accuracy, and therefore provides a more reliable basis for the extraction of decision-making rules.