The approach to classifying multi-output datasets based on cluster validity index method

Abstract

A cluster validity index (CVI) classification method is applied to enhance the performance of existing Multiple-Attribute Decision Making (MADM) method. This paper proposed index-based method is called the FRM-index method which combined Fuzzy Set (FS), Rough Set (RS), and a cluster validity index function. The effectiveness of the proposed FRM-index method is evaluated by comparing the classification results obtained for the relating UCI datasets using a statistical approach. Overall, the results show that the proposed method not only provides a more reliable basis for the extraction of decisionmaking rules for multi-output datasets, but also fills out the uncertainty and facilitates an effective MADM built.