Mining fuzzy rules from quantitative data based on the AprioriTid algorithm

Most of conventional data mining algorithms identify the relation among transactions with binary values. Transactions with quantitative values are, however, commonly seen in real world applications. This paper thus attempts to propose a new datamining algorithm to enhance the capability of exploring interesting knowledge from the transactions with quantitative values. The proposed algorithm integrates the fuzzy set concepts and the AprioriTid mining algorithm to find interesting fuzzy association rules from given transaction data. The database needs to be scanned only in the first pass to calculate the support of the items. Experiments on students' grades in I-Shou University are also made to verify the performance of the proposed algorithm.

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