Decomposable Families of Itemsets

TL;DR

This paper introduces decomposable families of itemsets that form a probabilistic model and a junction tree structure, enabling efficient and high-quality pattern selection from large collections.

Contribution

It proposes a novel approach using decomposable families and junction trees for pattern selection, with an efficient algorithm and practical application example.

Findings

Algorithm produces high-quality pattern subsets

Junction trees offer intuitive representation

Model enables efficient querying

Abstract

The problem of selecting a small, yet high quality subset of patterns from a larger collection of itemsets has recently attracted lot of research. Here we discuss an approach to this problem using the notion of decomposable families of itemsets. Such itemset families define a probabilistic model for the data from which the original collection of itemsets has been derived from. Furthermore, they induce a special tree structure, called a junction tree, familiar from the theory of Markov Random Fields. The method has several advantages. The junction trees provide an intuitive representation of the mining results. From the computational point of view, the model provides leverage for problems that could be intractable using the entire collection of itemsets. We provide an efficient algorithm to build decomposable itemset families, and give an application example with frequency bound querying using the model. Empirical results show that our algorithm yields high quality results.