Discovery of Sequential Event Patterns based on Interestingness

Abstract

Sequential pattern mining methods efficiently discover all frequent sequential patterns by using apriori property. However, analysts are not always interested in frequent patterns, because the patterns are common and the analysts cannot get new knowledge from the patterns. The paper proposes a new criterion which discovers interesting patterns for the analysts. The paper shows that the criterion satisfies the apriori property and how the criterion is related to existing criteria: support and confidence. Also, the paper proposes an efficient sequential pattern mining method based on the proposed criterion. Moreover, the paper shows its effect by applying it to daily business reports given from a sales force automation system.