Mining Fuzzy Multidimensional Association Rules Using Fuzzy Decision Tree Induction Approach*

Abstract: Mining fuzzy multidimensional association rules is one of the important processes in data mining application. This paper extends the concept of Decision Tree Induction (DTI) dealing with fuzzy value in order to express human knowledge for mining fuzzy multidimensional association rules. Decision Tree Induction (DTI), one of the Data Mining classification methods, is used in this research for predictive problem solving in analyzing patient medical track records. Meaningful fuzzy labels (using fuzzy sets) can be defined for each domain data. For example, fuzzy labels poor disease, moderate disease, and severe disease are defined to describe a condition/type of disease. We extend and propose a concept of fuzzy information gain to employ the highest information gain for splitting a node. In the process of generating fuzzy multidimensional association rules, we propose some fuzzy measures to calculate their support, confidence and correlation. The designed application gives a significant contribution to assist decision maker for analyzing and anticipating disease epidemic in a certain area