Introduction of Item Constraints to Discover Characteristic Sequential Patterns

Abstract

This chapter introduces a method that discovers characteristic sequential patterns from sequential data based on background knowledge. The sequential data is composed of rows of items. This chapter focuses on the sequential data based on the tabular structured data. That is, each item is composed of an attribute and an attribute value. Also, this chapter focuses on item constraints in order to describe the background knowledge. The constraints describe the combination of items included in sequential patterns. They can represent the interests of analysts. Therefore, they can easily discover sequential patterns coinciding to the interests of the analysts as characteristic sequential patterns. In addition, this chapter focuses on the special case of the item constraints. It is constrained at the last item of the sequential patterns. The discovered patterns are used to the analysis of cause, and reason and can predict the last item in the case that the sub-sequence is given. This chapter introduces the property of the item constraints for the last item.