Mining association rules for RFID data with concept hierarchy

Recently, radio frequency identification (RFID) technology is being deployed for several applications, including supply-chain optimization, business process automation, asset tracking, and problem traceability applications. The problem with RFID data is that its degree increases according to time and location, thus, resulting in an enormous volume of data duplication. Therefore, it is difficult to extract useful hidden knowledge in RFID data using traditional association rule mining techniques, or analyze data using statistical techniques or queries. This paper suggest association rule generation method based on the meta rule which could find a meaningful rule by using inclusion relation and concept hierarchy between data, in order to extract a hidden pattern from RFID data. Therefore, we could not only eliminate the duplicated rule efficiently by using meta-rule but also reduce the complexity by processing the limited association rule examination. Also, this method is useful to improve the storage efficiency and to find a hidden association relationship between objects.