Detecting temporally redundant association rules

Methods for association rule discovery and pruning assume implicitly that the associations hidden in the data are stable over time and thus provide a rather static view on data and their underlying structure. This is unrealistic in time-stamped domains, which are standard for real life business data. The question "which association rules exist?" is replaced by "how do properties of association rules change?" In order to cope with the vast number of detectable rule changes, preprocessing techniques are required that find those rules which are root cause to interesting rule changes. The paper proposes an approach based on statistical tests that finds derivative rule change histories and marks the respective rules as redundant. The effectiveness in reducing the number of rule histories is demonstrated using real life survey data.