Assessing the interestingness of temporal rules with Sequential Implication Intensity

In this article, we study the assessment of the interestingness of sequential rules (generally temporal rules). This is a crucial problem in sequence analysis since the frequent pattern mining algorithms are unsupervised and can produce huge amounts of rules. While association rule interestingness has been widely studied in the literature, there are few measures dedicated to sequential rules. Continuing with our work on the adaptation of implication intensity to sequential rules, we propose an original statistical measure for assessing sequential rule interestingness. More precisely, this measure named Sequential Implication Intensity (SII) evaluates the statistical significance of the rules in comparison with a probabilistic model. Numerical simulations show that SII has unique features for a sequential rule interestingness measure.