Data Mining and Knowledge Discovery

, Volume 9, Issue 3, pp 223–248

Mining Non-Redundant Association Rules

  • Mohammed J. Zaki
Article

DOI: 10.1023/B:DAMI.0000040429.96086.c7

Cite this article as:
Zaki, M.J. Data Mining and Knowledge Discovery (2004) 9: 223. doi:10.1023/B:DAMI.0000040429.96086.c7

Abstract

The traditional association rule mining framework produces many redundant rules. The extent of redundancy is a lot larger than previously suspected. We present a new framework for associations based on the concept of closed frequent itemsets. The number of non-redundant rules produced by the new approach is exponentially (in the length of the longest frequent itemset) smaller than the rule set from the traditional approach. Experiments using several “hard” as well as “easy” real and synthetic databases confirm the utility of our framework in terms of reduction in the number of rules presented to the user, and in terms of time.

association rule miningfrequent closed itemsetsformal concept analysis

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Mohammed J. Zaki
    • 1
  1. 1.Computer Science DepartmentRensselaer Polytechnic InstituteNYUSA