Mining Top-n Local Outliers in Constrained Spatial Networks
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Intelligent process control system with RFID cuboid
Proceedings of the 11th International Conference on Electronic Commerce
Improving the performance of fuzzy clustering algorithms through outlier identification
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Developing RFID database models for analysing moving tags in supply chain management
ER'11 Proceedings of the 30th international conference on Conceptual modeling
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Radio Frequency Identification (RFID) applications are emerging as key components in object tracking and supply chain management systems. In next future almost every major retailer will use RFID systems to track the shipment of products from suppliers to warehouses. Due to RFID readings features this will result in a huge amount of information generated by such systems when costs will be at a level such that each individual item could be tagged thus leaving a trail of data as it moves through different locations. We define a technique for efficiently detecting anomalous data in order to prevent problems related to inefficient shipment or fraudulent actions. Since items usually move together in large groups through distribution centers and only in stores do they move in smaller groups we exploit such a feature in order to design our technique. The preliminary experiments show the effectiveness of our approach.