# Probably the Best Itemsets

## TL;DR

This paper introduces a Bayesian-based framework for evaluating itemset quality, enabling efficient discovery of high-quality, interpretable itemsets with a focus on controlling pattern explosion.

## Contribution

It presents a novel Bayesian measure for itemset quality, utilizing exponential models and MCMC sampling to improve interpretability and manage pattern explosion.

## Key findings

- Effective in synthetic data
- Produces insightful itemsets in real-world data
- Demonstrates practical applicability

## Abstract

One of the main current challenges in itemset mining is to discover a small set of high-quality itemsets. In this paper we propose a new and general approach for measuring the quality of itemsets. The method is solidly founded in Bayesian statistics and decreases monotonically, allowing for efficient discovery of all interesting itemsets. The measure is defined by connecting statistical models and collections of itemsets. This allows us to score individual itemsets with the probability of them occuring in random models built on the data. As a concrete example of this framework we use exponential models. This class of models possesses many desirable properties. Most importantly, Occam's razor in Bayesian model selection provides a defence for the pattern explosion. As general exponential models are infeasible in practice, we use decomposable models; a large sub-class for which the measure is solvable. For the actual computation of the score we sample models from the posterior distribution using an MCMC approach. Experimentation on our method demonstrates the measure works in practice and results in interpretable and insightful itemsets for both synthetic and real-world data.