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Mining association rules from quantitative data
Affiliation:1. School of Electrical and Information Engineering, Tianjin University, China;2. Center for OPTical IMagery Analysis and Learning, Northwestern Polytechnical University, China
Abstract:Data-mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values, however, transactions with quantitative values are commonly seen in real-world applications. This paper thus proposes a new data-mining algorithm for extracting interesting knowledge from transactions stored as quantitative values. The proposed algorithm integrates fuzzy set concepts and the apriori mining algorithm to find interesting fuzzy association rules in given transaction data sets. Experiments with student grades at I-Shou University were also made to verify the performance of the proposed algorithm.
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