Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining

TL;DR

This paper introduces a unifying method to identify and remove misleading, redundant association rules called specious rules, significantly improving the quality of association rule mining results.

Contribution

It provides a theoretical framework and practical algorithms for detecting and pruning specious rules, addressing a broad class of misleading associations in data mining.

Findings

Reduces the number of misleading association rules

Effective across multiple goodness measures

Improves the interpretability of mined rules

Abstract

We present theoretical analysis and a suite of tests and procedures for addressing a broad class of redundant and misleading association rules we call \emph{specious rules}. Specious dependencies, also known as \emph{spurious}, \emph{apparent}, or \emph{illusory associations}, refer to a well-known phenomenon where marginal dependencies are merely products of interactions with other variables and disappear when conditioned on those variables. The most extreme example is Yule-Simpson's paradox where two variables present positive dependence in the marginal contingency table but negative in all partial tables defined by different levels of a confounding factor. It is accepted wisdom that in data of any nontrivial dimensionality it is infeasible to control for all of the exponentially many possible confounds of this nature. In this paper, we consider the problem of specious dependencies in the context of statistical association rule mining. We define specious rules and show they offer a unifying framework which covers many types of previously proposed redundant or misleading association rules. After theoretical analysis, we introduce practical algorithms for detecting and pruning out specious association rules efficiently under many key goodness measures, including mutual information and exact hypergeometric probabilities. We demonstrate that the procedure greatly reduces the number of associations discovered, providing an elegant and effective solution to the problem of association mining discovering large numbers of misleading and redundant rules.