A new method for ranking association rules with multiple criteria based on dominance relation
Azzeddine Dahbi, Siham Jabri, Youssef Balouki, Taoufiq Gadi · 2016
Datamining is the process of extracting interesting information of patterns from large databases. One of the most important datamining task and well-researched is the association rules mining. It aims to find the interesting correlation and relations among sets of items in the transaction databases. One of the main problems related to the discovery of these associations that a decision maker faces is the huge number of association rules extracted. Various measures propose to evaluate the extracted association rules. Currently there is no optimal measure, and there is no measure is better than others. To solve this challenge we propose an approach based on dominance relation aiming to find a good compromise without favoring or excluding any measures by applying a value to each rule which permit to ranking them. The experiments performed on benchmark datasets, show a significant performance of the proposed approach.