Mining Best Closed Itemsets for Projection-antimonotonic Constraints in Polynomial Time

The exponential explosion of the set of patterns is one of the main challenges in pattern mining. This chalenge is approached by introducing a constraint for pattern selection. One of the first constraints proposed in pattern mining is support (frequency) of a pattern in a dataset. Frequency is an anti-monotonic function, i.e., given an infrequent pattern, all its superpatterns are not frequent. However, many other constraints for pattern selection are neither monotonic nor anti-monotonic, which makes it difficult to generate patterns satisfying these constraints. In order to deal with nonmonotonic constraints we introduce the notion of “projection antimonotonicity” and Σοφια algorithm that allow generating best patterns for a class of nonmonotonic constraints. Cosine interest, robustness, stability of closed itemsets, and the associated Δ-measure are among these constraints. Σοφια starts from light descriptions of transactions in dataset (a small set of items in the case of itemset description) and then iteratively adds more information to these descriptions (more items with indication of tidsets they describe). In the experiments, we compute best itemsets w.r.t. some measures and show the advantage of our approach over postpruning approaches.