Incremental Fuzzy Association Rule Mining for Classification and Regression

The aim of mining fuzzy association rules is to find both the association and the casual relationships between the itemsets. With the arrival of dynamic data, the fuzzy association rules should be updated in real time. However, most of the existing algorithms must remine the updated database and can only be applied in classification. This paper proposes an incremental fuzzy association rule mining algorithm to solve classification and regression problems. First, the sliding window is adopted to divide the fuzzy dataset. Second, the dynamic fuzzy variable selection algorithm is adopted to select variables for reducing the search space of the fuzzy association rule mining. Finally, in each sliding window, the result of variable selection is used to incrementally mine the causal fuzzy association rules with the fuzzy Eclat algorithm. When new data are added, the process judges whether concept drift occurs, and if so, the rule set is updated; otherwise, the original rule set is still applied. The weights of the rules are calculated to find the evolving relationship. The simulation result shows that this algorithm can improve accuracy and efficiency.