Integrate Variable Precision Rough Sets and modified PBMF index function for partitioning and classifying complex datasets
Kuang Yu Huang, Yu-Hsin Cheng · 2011
This study proposes a method for partitioning and classifying complex datasets using a hybrid method based on Fuzzy C-Means (FCM) method, Variable Precision Rough Set (VPRS) theory and a modified form of the PBMF index function (a cluster validity index function). The proposed VPRS index method partitions the attributes within the dataset rather than the data and achieves both the optimal number of clusters and the optimal classification accuracy. The validity of the proposed approach is confirmed by comparing the clustering results obtained from the VPRS method for a hypothetical function and a typical stock market system with those obtained from the conventional RS and PBMF methods, respectively. Overall, the results show that the VPRS index method not only has a better clustering performance than the PBMF method, but also achieves greater classification accuracy, and therefore provides a more reliable basis for the extraction of decision-making rules.