Efficient Mining Support-Confidence Based Framework Generalized Association Rules

Abstract

Mining association rules are one of the most critical data mining problems, intensively studied since their inception. Several approaches have been proposed in the literature to extend the basic association rule framework to extract more general rules, including the negation operator. Thereby, this extension is expected to bring valuable knowledge about an examined dataset to the user. However, the efficient extraction of such rules is challenging, especially for sparse datasets. This paper focuses on the extraction of literalsets, i.e., a set of present and absent items. By consequence, generalized association rules can be straightforwardly derived from these literalsets. To this end, we introduce and prove the soundness of a theorem that paves the way to speed up the costly computation of the support of a literalist. Furthermore, we introduce FasterIE, an efficient algorithm that puts the proved theorem at work to efficiently extract the whole set of frequent literalets. Thus, the FasterIE algorithm is shown to devise very efficient strategies, which minimize as far as possible the number of node visits in the explored search space. Finally, we have carried out experiments on benchmark datasets to back the effectiveness claim of the proposed algorithm versus its competitors.

A genetic algorithm coupled with tree-based pruning for mining closed association rules

“…A combination of Apriori, rough set, and fuzzy techniques were employed to detect fraudulent credit card transactions [38]. To extract association rules from sparse datasets Mouakher et al [39] presented a technique that relied on the extraction of literal sets. An algorithm FasterIE has been designed that minimizes the number of nodes visited in the search space thereby improving the efficiency of mining itemsets.…”