Towards Improving Solution Dominance with Incomparability Conditions: A case-study using Generator Itemset Mining

TL;DR

This paper introduces an extension to dominance programming by incorporating incomparability conditions, enhancing the efficiency of pattern mining by reducing dominated solutions and post-processing efforts.

Contribution

It proposes a generic framework with incomparability conditions for dominance programming, extending the ESSENCE language and demonstrating its effectiveness in generator itemset mining.

Findings

Improved search efficiency with incomparability conditions

Reduced post-processing to filter dominated solutions

Preliminary results show performance gains in generator itemset mining

Abstract

Finding interesting patterns is a challenging task in data mining. Constraint based mining is a well-known approach to this, and one for which constraint programming has been shown to be a well-suited and generic framework. Dominance programming has been proposed as an extension that can capture an even wider class of constraint-based mining problems, by allowing to compare relations between patterns. In this paper, in addition to specifying a dominance relation, we introduce the ability to specify an incomparability condition. Using these two concepts we devise a generic framework that can do a batch-wise search that avoids checking incomparable solutions. We extend the ESSENCE language and underlying modelling pipeline to support this. We use generator itemset mining problem as a test case and give a declarative specification for that. We also present preliminary experimental results on this specific problem class with a CP solver backend to show that using the incomparability condition during search can improve the efficiency of dominance programming and reduces the need for post-processing to filter dominated solutions.