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- Mining association rules is an important topic in the data mining research. 关联规则采掘是数据采掘中重要的研究课题。
- Mining association rules require two pieces of data, the transaction and what was bought in that transaction. 挖掘关联规则需要两方面的数据,事务及该事务中所包含的信息。
- Mining association rules in a transaction database is a problem with great value. 交易数据库中的关联规则挖掘是一个很有价值的问题。
- Discovering frequent itemsets is a key problem in data mining association rules. 发现频繁项目集是关联规则数据开采中的关键问题。
- In order to find the useful association rules,the interestingness of mining association rules is discussed. 利用模板将用户感兴趣的规则和不感兴趣的规则区分开,以此来完成关联规则有趣性的主观评测;
- Mining association rules in transaction databases has been studied popularly in data mining research. 摘要关联规则挖掘方法是数据挖掘领域的一个研究热点。
- Apriori algorithm and FP-growth algorithm are two main algorithms in mining association rules which are based on frequent itremsets. 关联规则的挖掘主要是基于频繁集的方法,相关的算法主要有Apriori算法和FP-growth算法。
- Agrawal R,Srikant R.Fast algorithms for mining association rules[Z].Proc. Of the 20th VLDB Conference Santiago,Chile,1994. 铁治欣;陈奇;俞瑞钊.;关联规则采掘综述
- One is to modify the Apriori algorithm to mine association rules between querying keywords and browsing websites. 二是以某些关键字为探勘的目标,来撷取前置项目组为这些关键字的关联规则。
- At present,lots of algorithms for mining association rules have been brought forward.The most famous algorithms are Apriori and its transfiguration. 目前,已经提出了许多挖掘关联规则的算法,其中最著名的是Apriori算法及其变形。
- Description of mining association rules is then given, which is followed by Apriori algorithms and DHP algorithms, both of the algorithms are simulated. 接下来对关联规则的数据挖掘进行了阐述,并分别对Apriori算法和DHP算法进行了仿真研究;
- Basic theory of Association Rules is researched. The typical algorithms such as Apriori and FP-growth of mining association rules are discussed and analyzed. 研究了关联规则的基本理论,描述并分析了经典关联规则算法Apriori算法和FP-growth算法。
- To the deficiency of Apriori algorithm, this dissertation brings forward a high-efficient algorithm for mining association rule. 针对Apriori算法的不足,提出了一种新的关联规则的高效挖掘算法。
- The paper presents a new algorithm that embodies the factor of time for mining association rules in stock linkage information based on Apriori algorithm. 在数据挖掘的模式中,关联规则是其中比较重要的一种。
- After the method for mining association rules is analyzed,a dynamic algorithm of frequent itemsets mining based on undirected itemsets graph is put forward. 通过对关联规则挖掘算法的详细分析,提出了一种基于无向项集图的动态频繁项集挖掘算法。
- A.Savasere, E.Omiecinski, and S.Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases”, Proc. of 21st VLDB, pp.432-444, 1995. 陈彦良、陈家仁,在限定项目个数与交易长度的资料库中挖掘关联规则,国立中央大学资讯管学系硕士论文,民国90年。
- Finally, On the basis of the mining association rule algorithm, coupled with the newly-raised algorithm and technology, a prototype is designed, which is a Chinese concept-based information retrieval based mining association rules. 本文在已有关联规则挖掘算法的基础上,结合所提出的改进算法及技术,最后实现了一个基于关联规则挖掘的中文概念检索系统原型。
- After understanding and analysis of association rules algorithm, this paper studies the algorithm of data mining association rules and makes some improvement to the APriori algorithm. 本文从理解关联规则算法和对其分析出发,研究数据挖掘的关联规则算法及Apriori算法的改进。
- With the aid of the analysis of mining association rules,a method of mining association rules based on reducing transaction is proposed,which makes best use of property of Apriori. 通过对Apriori算法挖掘过程进行分析,提出了一种基于事务压缩的关联规则挖掘算法。
- Agrawal, R., Imielinski T. &Swami, A., Mining Association Rules Between Sets of Items in Large Data bases.In proceedings Of the ACM SIGMOD Conference on Management of Data, Washington DC,USA, 1993. 谢秋珠,国中中辍复学生需求与辅导策略之研究,国立台湾师范大学公民教育与活动领导学系硕士论文,2003年。