Mining highly correlated least association rules using scalable trie-based algorithm

Development of least association rules (ARs) mining algorithms is one of the more challenging areas in data mining. Exclusive measurements, complexity and excessive computational cost are the main obstacles as compared to frequent pattern mining. Indeed, most previous studies still use the Apriori-like algorithms. To address this issue, this article proposes a new correlation measurement called definite factor (DF) and a scalable trie-based algorithm named significant least pattern growth (SLP-Growth). This algorithm generates the least patterns based on interval support and finally determines it significances using DF. Experiments with the real datasets show that the SLP-Growth can discover highly positive correlated and significant of least ARs. Indeed, it also outperforms the fast frequent pattern-Growth algorithm up to two times, thus verifying its efficiency.