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关联规则挖掘是数据挖掘研究领域中的一个重要任务,旨在挖掘事务数据库中有意义的关联。随着大量数据不停的收集和存储,从数据库中挖掘关联规则显得越来越有必要性,关联规则挖掘的Apriori算法是数据库挖掘的最经典算法并得到广泛应用,在介绍关联规则挖掘和Apriori算法的基础上,发现Apriori算法存在着产生候选项目集效率低和频繁扫描数据等缺点。综述了Apriori算法的主要优化方法,并指出了Apriori算法在实际中的应用领域,提出了未来Apriori算法的研究方向和应用发展趋势。
Abstract：Mining association rules,designed to tap the fun associated which obtained the transaction database,is an important task of data mining research field.With the kept capture and storage of large amount of data,mining association rules from the database plays more and more important role,the Apriori algorithm of mining association rules is the most classic one in database mining algorithms and widely used.On the base of description of mining association rules and the Apriori algorithm.Apriori algorithm is found to have drawbacks:the rate of generating candidate item sets is low and frequently scan data,and so on.The main optimization methods of the Apriori algorithm are overviewed,and practical applications of the Apriori algorithm are pointed out,the research directions and application trends of the Apriori algorithm in the future are proposed.
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基本信息:
DOI：
中图分类号:TP311.13
引用信息:
[1]赵洪英,蔡乐才,李先杰.关联规则挖掘的Apriori算法综述[J].四川理工学院学报(自然科学版),2011,24(01):66-70.
基金信息:
四川省科技厅支撑计划项目(2008FZ0109);; 四川省教育厅科技项目(2007ZL048)