Mining Optimal Utility Incorporated Sequential Pattern from RFID Data Warehouse Using Genetic Algorithm

Today, identification of sequential patterns from a huge database sequence is a major problem in the field of KDD. In addition, if the entire set of sequential patterns existing in a large database is presented, the user may find it difficult to understand and employ the mined result. In order to overcome these issues, we propose an efficient data mining system to generate the most favorable sequential patterns. The proposed technique first generates datasets from the warehoused RFID data. Each mined pattern has distinct utility and the most favorable sequential patterns are generated from the mined sequential patterns by using Genetic Algorithm (GA). A fitness function is used in GA to find out the sequential pattern that provides maximum profit. The implementation result shows that the proposed mining system accurately extracts the important RFID tags and its combinations, nature of movement of the tags and the optimum sequential patterns.

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