This paper deals with transactions with their classes. The classes represent the difference of conditions in the data collection. This\r\npaper redefines two kinds of supports: characteristic support and possible support. The former one is based on specific classes\r\nassigned to specific patterns. The latter one is based on the minimum class in the classes. This paper proposes a new method\r\nthat efficiently discovers patterns whose characteristic supports are larger than or equal to the predefined minimum support by\r\nusing their possible supports. Also, this paper verifies the effect of the method through numerical experiments based on the data\r\nregistered in the UCI machine learning repository and the RFID (radio frequency identification) data collected from two apparel\r\nshops.
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