A study of on-line adaption miner for interesting association rules

• Main Authors: Kao-hung Lin, 林高弘
• Other Authors: Hung-Pin Chiu
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/02691823957774509403

碩士 === 南華大學 === 資訊管理學研究所 === 91 === 　　In the real application environment, the content of databases grows quickly and incrementally; therefore, an on-line and adaptive miner, which can efficiently mine interesting association rules from the dynamic databases, is very necessary for helping the manager to make the correct decisions in order to promote the competition ability of businesses. The on-line and incremental mining of association rules are the process trying to achieve the requirements. 　 　　Besides, the mined association rules may be redundant or useless to the users so an interesting measure of rules is needed to eliminate the ineffective rules. In this study, we propose an adaptive miner based on the Correlative Reduced Itemset Lattice (CRIL). The proposed approach can dynamically adjust the CRIL with the different thresholds, the variation of databases, and the correlation of the itemsets to rapidly generate the interesting rules that the users really want. Several experiments were conducted to verify the effectiveness and feasibility of the proposed association rule miner.