A mining algorithm for fuzzy weighted association rules

• Published in: Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693) Vol. 4; pp. 2495 - 2499 Vol.4
• Main Authors: Bao-Yi Wang, Shao-Min Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2003
• Subjects: Association rules; Computer science; Data analysis; Data mining; Electronic mail; Fuzzy sets; Itemsets; Knowledge engineering; Power engineering and energy; Transaction databases
• ISBN: 0780378652; 9780780378650; 9780780381315; 0780381319
• DOI: 10.1109/ICMLC.2003.1259932

The association rule mining is an important research subject of knowledge discovery. Aiming at the common method of mining for attributes of quantitative type in database, we analyze the existing defects and put forward a method of applying fuzzy set theory to association rules mining. Due to the problem that each attribute's importance is different in specific purpose mining, we put forward a solution by assigning corresponding weight to attribute of different importance. Based on this idea, we put forward a mining algorithm using fuzzy weighted association rules and through the given experiment we testify the feasibility of the algorithm, and point out the existing defect of the algorithm demanding improvement in future.