Decision table reduction based on cluster analysis

Based on the rough set theory, a new systematic method is proposed to reduce the decision table and induce the decision-making rules. In order to avoid the shortcomings of current discretization methods, the cluster analysis of multi-variable statistics is introduced to discretize the continuous attributes in the decision table. With the dendrogram and three useful statistics, i.e. R², SPRSQ and PSF, the decision table is derived which can meet the requirement of the rough set theory. After that, the traditional algorithm of attributes reduction based on the discernibility matrix and logical operation is simplified, and an improving heuristic algorithm for attribute value reduction and decision-making rule induction is presented. Finally, an illustrative example is proposed to validate its feasibility and effectiveness.