Interactive Constrained Association Rule Mining

TL;DR

This paper explores methods for interactive association rule mining, allowing users to specify conditions and incrementally query associations, with new algorithms evaluated for performance.

Contribution

It introduces a novel approach combining query integration during mining and incremental querying, with multiple algorithms and performance comparisons.

Findings

Algorithms effectively support interactive querying

Performance varies with different algorithms

Enhanced user control over association rule mining

Abstract

We investigate ways to support interactive mining sessions, in the setting of association rule mining. In such sessions, users specify conditions (queries) on the associations to be generated. Our approach is a combination of the integration of querying conditions inside the mining phase, and the incremental querying of already generated associations. We present several concrete algorithms and compare their performance.