On the discovery of time distance constrained temporal association rules

Abstract

The increased use of data mining algorithms reflects the need for automatic extraction of knowledge from large volumes of data. This work presents a temporal data mining algorithm that discovers frequent Association Rules from timestamped data. These rules are named Cause-Effect Rules, each represented by a multiset of unordered events (Cause) followed by a singleton event (Effect). Also, a Cause-Effect Rule is valid within an specific constraint that defines the minimum and maximum time distance between its Cause and Effect. Our algorithm was tested on a data set from two hospital emergency departments in Sherbrooke, QC, Canada.