Class-association rules pruning using regularization

Abstract

Association rules mining is a data mining technique that seeks interesting associations between attributes from massive high-dimensional categorical feature spaces. However, as the dimensionality gets higher, the data gets sparser which results in the discovery of a large number of association rules and makes it difficult to understand and to interpret. In this paper, we focus on a particular type of association rules namely Class-Association Rules (CARs) and we introduce a new approach of Class-Association Rules pruning based on Lasso regularization. In this approach we propose to take advantage of variable selection ability of Lasso regularization to prune less interesting rules. The experimental analysis shows that the introduced approach gives better results than CBA in term of number as well as the quality of the obtained rules after pruning.