Due to increasing use of very large database and data warehouses, discovering useful knowledge from transactions is becoming an important research area. On the other hand, using fuzzy classification in data mining has been developed in recent years. Hong and Lee proposed a general learning method that automatically derives fuzzy if-then rules and membership functions from a set of given training examples using a decision table. But it is complex if there are many attributes or if the predefined unit is small. Hong and Chen improve it by first selecting relevant attributes and building appropriate initial membership functions. Based on Hong’s heuristic algorithm of membership functions and Apriori approach, we propose a fuzzy mining algorithm to explore association rules from given quantitative transactions. Experimental results on Iris data show that the proposed algorithm effectively induces more association rules.
Zhang, Lingling; Shi, Yong; and Yang, Xinhua, "A Fuzzy Mining Algorithm for Association-Rule Knowledge Discovery" (2005). AMCIS 2005 Proceedings. 121.