Algorithm for mining fuzzy association rule based on TD-FP-growth

An algorithm based on TD-FP-growth is proposed for mining fuzzy association rule, which uses three kinds of t-norm operator to calculate the support degree of fuzzy frequent items, and adopts corresponding implication operator to measure implication degree of fuzzy association rule. The association rule mined by the algorithm can express the logic semantic of graduality and certainty between fuzzy items. Each transaction's membership degree versus fuzzy item denoted by FP-tree's node is stored by hash technology, and each transaction's identifier is regarded as key value, which adapts TD-FP-growth to mine fuzzy frequent items. The time and space complexity of the algorithm are analyzed. The experimental results show that the algorithm is more effective than the fuzzy frequent item mining algorithm based on apriori in term of time.