Discovery of Characteristic Patterns from Transactions with Their Classes

Abstract EN

This paper deals with transactions with their classes.

The classes represent the difference of conditions in the data collection.

This paper redefines two kinds of supports: characteristic support and possible support.

The former one is based on specific classes assigned to specific patterns.

The latter one is based on the minimum class in the classes.

This paper proposes a new method that efficiently discovers patterns whose characteristic supports are larger than or equal to the predefined minimum support by using their possible supports.

Also, this paper verifies the effect of the method through numerical experiments based on the data registered in the UCI machine learning repository and the RFID (radio frequency identification) data collected from two apparel shops.