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

## TL;DR

This paper introduces the SOFIA algorithm for efficiently mining the best closed itemsets under projection-antimonotonic constraints, addressing the challenge of nonmonotonic pattern constraints in polynomial time.

## Contribution

It proposes the concept of projection antimonotonicity and the SOFIA algorithm to generate optimal patterns for nonmonotonic constraints in polynomial time.

## Key findings

- SOFIA effectively mines patterns satisfying nonmonotonic constraints.
- The approach handles constraints like cosine interest, robustness, and stability.
- Pattern generation is achieved in polynomial time.

## Abstract

The exponential explosion of the set of patterns is one of the main challenges in pattern mining. This challenge 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 SOFIA algorithm that allow generating best patterns for a class of nonmonotonic constraints. Cosine interest, robustness, stability of closed itemsets, and the associated delta-measure are among these constraints. SOFIA 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).