Sequential event prediction with association rules
Conference Paper
We consider a supervised learning problem in which data are revealed sequentially 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, 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, and 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. © 2011 C. Rudin, B. Letham, A. Salleb-Aouissi, E. Kogan & D. Madigan.
Duke Authors
Cited Authors
- Rudin, C; Letham, B; Salleb-Aouissi, A; Kogan, E; Madigan, D
Published Date
- January 1, 2011
Published In
Volume / Issue
- 19 /
Start / End Page
- 615 - 634
Electronic International Standard Serial Number (EISSN)
- 1533-7928
International Standard Serial Number (ISSN)
- 1532-4435
Citation Source
- Scopus