Mining Association Rules in Temporal Sequences

Mining association rules is an important technique for discovering meaningful patterns in datasets. Temporal association rule mining can be decomposed into two phases: finding temporal frequent patterns and finding temporal rules construction. Till date, a large number of algorithms have been proposed in the area of mining association rules. However, most of these algorithms consider patterns as a collection of point primitives and their three basic relations (<, =, >). Several applications consider patterns with duration and need to reason about intervals and their thirteen possible relationships. In this paper we investigate properties of temporal sequences represented as a collection of intervals. We present a simple framework for temporal sequence and describe DATTES (Discovering pATterns in TEmporal Sequences), an innovative algorithm using interval properties to mine temporal patterns. The framework can be used to mine temporal association rules. According to some interval algebra properties, this paper introduces a new confidence evaluation function for mining temporal rules. Experiments on real dataset (human face identification problem) show the effectiveness and the performances of this approach.