Association-rule knowledge discovery by using a fuzzy mining approach. (31st August 2006) - Zhang, Lingling; Shi, Yong; Yang, Xinhua
Due to increasing use of very large database and data warehouses, discovering useful knowledge from transactions is becoming an important research area. One of approaches is fuzzy classification. Hong and Lee (1996) proposed a learning method that automatically derives fuzzy if-then rules from a set of given training examples using a decision table. Hong and Chen (1999) improved it. Based on their heuristic algorithms and the well-known Apriori approach, this paper proposes a new 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.