A mining algorithm for fuzzy weighted association rules
Baoyi Wang, Shaomin Zhang · 2004
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.