A Relevance Criterion for Sequential Patterns

The theory of relevance is an approach for redundancy avoidance in labeled itemset mining. In this paper, we adapt this theory to the setting of sequential patterns. While in the itemset setting it is suggestive to use the closed patterns as representatives for the relevant patterns, we argue that due to different properties of the space of sequential patterns, it is preferable to use the minimal generator sequences as representatives, instead of the closed sequences. Thereafter, we show that we can efficiently compute the relevant sequences via the minimal generators in the negatives. Unlike existing iterative or post-processing approaches for pattern subset selection, our approach thus results both in a reduction of the set of patterns and in a reduction of the search space – and hence in lower computational costs.