An efficient discovery method of patterns from transactions with their classes

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 characteristic patterns based on the supports. Also, this paper verifies the effect of the method through numerical experiments based on the data registered in UCI machine learning repository.