Cluster Validity Indexes to Uncertain Data for Multi-Attribute Decision-Making Datasets

This paper proposes a novel function which is designated as the multi-attribute (MA) index function (derived from the conventional PBMF-index function), is used to evaluate the quality of the clustering solution in terms of the number of clusters assigned to each attribute and the accuracy of the corresponding Rough Set (RS) classification. The MA-index function processes a set of parameter values obtained from the Fuzzy C Mean method, Fuzzy Set theory, and RS theory. The MA-index function is embedded within an iterative procedure designated as a multi-attribute decision-making index method, which optimizes both the number of clusters per attribute in the dataset and the accuracy of the corresponding classification. In other words, the clustering/ classification outcome obtained from the multi-attribute decision making index method provides a suitable basis for the formation of reliable decision-making rules. On the whole, the outcomes reveal that the suggested technique not simply generates a much better clustering efficiency as compared to the single-attribute decision-making (SADM) and also PBMF techniques however additionally supplies a much more trustworthy basis for the removal of decision-making policies.