Discovery of Sequential Patterns Coinciding with Analysts’ Interests

Abstract

This paper proposes a new sequential pattern mining method. The method introduces a new evaluation criterion satisfying the Apriori property. The criterion is calculated by the frequency of the sequential pattern and the minimum frequency of items included in the items. It extracts sequential patterns that can be rules predicting future items with high probability. Also, the method introduces new constraints. The constraints extract item sets composed of items whose attributes are different and extracts sequential patterns composed of item sets whose attribute sets are equal to one another. The proposed method efficiently discovers sequential patterns coinciding with analysts’ interests by combining the criterion and the constraints. The paper verifies the effectiveness of the proposed method by applying it to medical examination data.