Mining Best Utility Pattern from RFID Data Warehouse through Genetic Alogotithm.
Identifying the sequential patterns from a huge database sequence is a main problem in the area of knowledge discovery and data mining. Therefore, only if an efficient mining technique is used the stored information will be helpful. In the earlier effort an innovative data mining technique based on sequential pattern mining and fuzzy logic was used to efficiently mine the RFID data. In a large database, if the entire set of sequential patterns is presented in the result the user may find it difficult to understand and employ the mining result. It is found that even efficient algorithms that have been proposed for mining large amount of sequential patterns from huge databases is a computationally costly task. An efficient data mining system that generates the most favorable sequential pattern is proposed to overcome this issue. Developing a utility considered RFID data mining technique is the main aim of exploration. Generation of dataset from the warehoused RFID data is the first stage in the proposed technique. Then, with various pattern length combinations the sequential patterns are mined and by using the sequential patterns the fuzzy rules are generated. Each pattern has its own utility. From the mined sequential patterns the most favorable sequential pattern is generated by using Genetic Algorithm (GA). To find out the sequential pattern with maximum profit, the fitness function of the GA will be used. The implementation result shows that the proposed mining system performs accurately by extracting the important RFID tags and its combinations, nature of movement of the tags and the optimum sequential patterns. Focusing only on the consequential sequential patterns that the users find interesting leads to productive trade in RFID enabled applications. Read More