Managing interesting rules in sequence mining

Abstract

The goal of sequence mining is the discovery of interesting sequences of events. Conventional sequence miners discover only frequent sequences, though. This limits the applicability scope of sequence mining for domains like error detection and web usage analysis. We propose a framework for discovering and maintaining interesting rules and beliefs in the context of sequence mining. We transform frequent sequences discovered by a conventional miner into sequence rules, remove redundant rules and organize the remaining ones into interestingness categories, from which unexpected rules and new beliefs are derived.