Episode-Rule Mining with Minimal Occurrences via First Local Maximization in Confidence

Abstract

An episode rule of associating two episodes represents a temporal implication of the antecedent episode to the consequent episode. Episode-rule mining is a task of extracting useful patterns/episodes from large event databases. We present an episode-rule mining algorithm for finding frequent and confident serial-episode rules via first local-maximum confidence in yielding ideal window widths, if exist, in event sequences based on minimal occurrences constrained by a constant maximum gap. Results from our preliminary empirical study confirm the applicability of the episode-rule mining algorithm for Web-site traversal-pattern discovery, and show that the first local maximization yielding ideal window widths exists in real data but rarely in synthetic random data sets.