Mining Fuzzy Multidimensional Association Rules Using Fuzzy Decision Tree Induction Approach

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.
Keywords: Data Mining, Classification, Decision Tree
Induction, Fuzzy Set, Fuzzy Association Rules.