Sequential pattern mining based on a new criteria and attribute constraints

This paper proposes the sequential interestingness as a new evaluation criterion that evaluates a sequential pattern corresponding to the interests of analysts. The sequential pattern is composed of rows of item sets. The criterion satisfies the apriori property. Also, this paper proposes three attribute constraints. These constraints can naturally evaluate relationships of attributes both in an item set and between continuous item sets. In addition, this paper proposes a mining method incorporating the criterion and the constraints. The method can efficiently discover all sequential patterns whose sequential interestingness is larger than or equal to a threshold and that satisfy the constraints. Lastly, this paper verifies the effectiveness of the proposed method by applying the method to medical examination data.