A Learning Theory Framework for Association Rules and Sequential Events

Abstract

We present a framework and generalization analysis for the use of association rules in the setting of supervised learning. We are specifically interested in a sequential event prediction problem where data are revealed one by one, and the goal is to determine what will next be revealed. In the context of this problem, algorithms based on association rules have a distinct advantage over classical statistical and machine learning methods; however, to our knowledge there has not previously been a theoretical foundation established for using association rules in supervised learning. We present two simple algorithms that incorporate association rules. These algorithms can be used both for sequential event prediction and for supervised classification. We provide generalization guarantees on these algorithms based on algorithmic stability analysis from statistical learning theory. We include a discussion of the strict minimum support threshold often used in association rule mining, and introduce an "adjusted confidence" measure that provides a weaker minimum support condition that has advantages over the strict minimum support. The paper brings together ideas from statistical learning theory, association rule mining and Bayesian analysis.