An efficient interestingness based algorithm for mining association rules in medical databases

Abstract

Mining association rules is an important area in data mining. Massively increasing volume of data in real life databases has motivated researchers to design novel and efficient algorithm for association rules mining. In this paper, we propose an association rule mining algorithm that integrates interestingness criteria during the process of building the model. One of the main features of this approach is to capture the user background knowledge, which is monotonically augmented. We tested our algorithm and experiment with some public medical datasets and found the obtained results quite promising. © 2007 Springer.