Ontology-driven Rule Generalization and Categorization for Market Data

Radio Frequency Identification (RFID) is an emerging technique that can significantly enhance supply chain processes and deliver customer service improvements. RFID provides user with efficient tracking on the flow of products throughout the wholesale process. However, the large number of information that has been generated from such a process creates difficulty in extracting and analyzing useful information. In this paper, we propose a method to mine the large data sets that allows smaller and more relevant search space compared to the original data sets. Our work is constructed from the following approaches: ontology-driven rule generalization which concentrates on controlling the level of items, and rule categorization using hierarchical association rule clustering that group the generated rules from the given problem space into hierarchical search space. The detailed steps for rule generalization based on ontologies sire presented, as well as the algorithms for rule categorization using hierarchical association rule clustering is developed. Our experiment proves the feasibility of our work which shows the significant reduction of the search space by decreasing the number of rules to be looked at and increasing the relevance among the rules.