Efficient mining of categorized association rules in large databases

S. Tseng*

*此作品的通信作者

研究成果: Conference article同行評審

摘要

A number of studies have been made on discovering association rules in a large database due to the wide applications. The common goal of the studies focused on finding the associated occurrence patterns between all items in a database. In practice, mining the association rules with the granularity as fine as an item could result in a huge number of rules that are too large to utilize efficiently. In practical applications, the users may be more interested in the associations between the categories the items belong to. In this paper, we propose a new method for mining categorized association rules efficiently by using compressed feature vectors. With the proposed method, at most one scan of the database is needed to produce the categorized association rules in each user query even under different mining parameters. Furthermore, the calculation time during the mining process is also reduced greatly by using only the simple logic operations on feature vectors. Hence, the overall performance in mining categorized association rules could be improved substantially.

原文English
頁(從 - 到)3606-3610
頁數5
期刊Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
5
DOIs
出版狀態Published - 2000
事件2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA
持續時間: 8 10月 200011 10月 2000

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