An Alternative Measure for Mining Weighted Least Association Rule and Its Framework

Mining weighted based association rules has received a great attention and consider as one of the important area in data mining. Most of the items in transactional databases are not always carried with the same binary value. Some of them might associate with different level of important such as the profit margins, weights, etc. However, the study in this area is quite complex and thus required an appropriate scheme for rules detection. Therefore, this paper proposes a new measure called Weighted Support Association Rules (WSAR*) measure to discover the significant association rules and Weighted Least Association Rules (WELAR) framework. Experiment results shows that the significant association rules are successfully mined and the unimportant rules are easily differentiated. Our algorithm in WELAR framework also outperforms the benchmarked FP-Growth algorithm.