[1]孟彩霞.频繁模式挖掘的约束算法[J].智能系统学报,2009,4(2):142-147.
MENG Cai-xia.A frequent pattern mining algorithm based on constraints[J].CAAI Transactions on Intelligent Systems,2009,4(2):142-147.
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MENG Cai-xia.A frequent pattern mining algorithm based on constraints[J].CAAI Transactions on Intelligent Systems,2009,4(2):142-147.
频繁模式挖掘的约束算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
4
期数:
2009年第2期
页码:
142-147
栏目:
学术论文—人工智能基础
出版日期:
2009-04-25
- Title:
- A frequent pattern mining algorithm based on constraints
- 文章编号:
- 1673-4785(2009)02-0142-06
- Keywords:
- frequent patterns mining; dynamic constraints; frequent item set; maximal frequent pattern
- 分类号:
- TP311
- 文献标志码:
- A
- 摘要:
- 在频繁模式挖掘过程中能够动态改变约束的算法比较少.提出了一种基于约束的频繁模式挖掘算法MCFP.MCFP首先按照约束的性质来建立频繁模式树，并且只需扫描一遍数据库，然后建立每个项的条件树，挖掘以该项为前缀的最大频繁模式，并用最大模式树来存储，最后根据最大模式来找出所有支持度明确的频繁模式.MCFP算法允许用户在挖掘频繁模式过程中动态地改变约束.实验表明，该算法与iCFP算法相比是很有效的.
- Abstract:
- Most algorithms don’t allow users to dynamically change constraints in the process of mining frequent patterns. A new algorithm, constrainbased frequent patterns mining, was developed to provide frequent pattern mining with constraints. First, the algorithm constructs the FPtree (frequent pattern tree) according to the descending or ascending order of constraints, and in this process the database only needs to be scanned once. Secondly, the conditional tree of each item was established to mine maximal frequent pattern with this term as a prefix, and the maximal frequent patterns were stored. Finally, all frequent patterns with precise support degrees were discovered according to the maximal frequent patterns. The significance of this method is that this algorithm allows users to dynamically change constraints during the process. Experimental outcomes showed that the proposed algorithm is more efficient than the algorithm of iCFP.
- 参考文献/References:
-
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备注/Memo
收稿日期：2008-12-16.
基金项目：陕西省自然科学基金资助项目(2004f283); 西安市科技创新支撑—应用发展研究计划资助项目(YF07024)
作者简介：
孟彩霞，女，1966年生，副教授，主要研究方向为数据库、数据挖掘等 .
E-mail:mcxmcx@xiyou.edu.cn
更新日期/Last Update:
2009-05-04