| Summary: | 碩士 === 東海大學 === 資訊工程與科學系 === 91 === Data mining is a very important issue; the association rule mining is the mostly studied one due to the wide applications among the proposed mining methods. The association mining problem should be proceeding by efficient algorithm; it could be divided into two phases. Phase 1: mining frequent itemsets from database. Phase 2: using frequent itemsets to generate the association rules. The proposed algorithm is based on stratified sampling and moment-preserving thresholding approach. Because of the reducing size of dataset, the proposed algorithm is efficient for the association rule mining problem. Moreover, we considered the buying quantities of items in support counting phase to increase the persuasion of frequent itemsets and association rules. The proposed algorithm has five steps. Step 1: generating transaction database by our simulator. Step 2: using moment-preserving thresholding approach to classify transaction by profit. Step 3: using stratified sampling to draw sample database. Step 4: mining frequent itemsets in sample database by Apriori algorithm. Step 5: generating association rules by frequent itemsets. By way of simulation results, the proposed algorithm is efficient in mining frequent itemsets. Besides, the association rules generated by proposed quantitative support counting method are more valuable.
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