[1]杨文杰,胡明昊,唐振民,等.一种有效的基于约束的关联规则发现算法[J].南京理工大学学报(自然科学版),2005,(01):109-112.
 YANG Wen-jie,HU Ming-hao,TANG Zhen-min,et al.Effective Algorithm for Mining Constrained Association Rules[J].Journal of Nanjing University of Science and Technology,2005,(01):109-112.
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一种有效的基于约束的关联规则发现算法
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
期数:
2005年01期
页码:
109-112
栏目:
出版日期:
2005-02-28

文章信息/Info

Title:
Effective Algorithm for Mining Constrained Association Rules
作者:
杨文杰胡明昊唐振民杨静宇
南京理工大学计算机科学与技术系, 江苏南京210094
Author(s):
YANG Wen-jie HU Ming-haoTANG Zhen-min YANG Jing-yu
Department of Computer Science and Technology, NUST, Nanjing 210094, China
关键词:
数据挖掘 关联规则 项约束
Keywords:
datam ining association rules item constra int
分类号:
TP311.1
摘要:
基于约束的关联规则挖掘是针对特定约束的规则的挖掘,挖掘的结果有着更好的针对性和实用性,Separate算法是现有的效果较好的算法,但有 2点不足:未修剪生成的候选集和候选项重复生成。对此该文提出了改进的SeparateP算法,算法中加入了对候选集的修剪,并且利用了项集重复生成的信息,使候选集的修剪更加有效快捷。实验表明,改进算法显著提高了原算法的效率。
Abstract:
M in ing constrained association ru les m ines some spec ial constrained rules and the resu lts are more pertain ing and practica.l Separate A lgorithm is a good algorithm to m ine constrained association rules and has two m ain shortages. One is that it scans candidate itemsets w ithout pruning; the other is that it creates candidate items redundantly. An improved algorithm, Separate P, is presented. The pruning on candidate sets is added to the a lgorithm, and Separate P makes fu ll use of the in fo rmation that the item sets create repeatedly. The prun ing is mademore effic ient and faster. The experimen tal resu lts show that Separate P can increase the Separate effic iency.

参考文献/References:

[ 1] Agraw a lR, S rikant R. Fast a lgor ithm s fo rm ining assoc-i ation rules [ A ] . Proceed ing o f the 20 th Internationa l Conference on Ve ry Large Database [ C ]. Santiago: [ s. n. ], 1994, 487- 499.
[ 2] 崔立新, 苑森淼, 赵春喜. 约束性相联规则发现方法及算法[ J]. 计算机学报, 2000, 23( 2): 216- 220.
[ 3] 寇育敬, 王春华, 黄厚宽. 约束关联规则的增量式维护算法[ J]. 计算机研究与发展, 2001, 38( 8): 947- 951.
[ 4]" Dense" Da ta Sets [ DB /OL]. http: / /www. almaden. ibm. com /so ftw are /quest /resources / index. shtm .l 2002 - 07- 19.

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备注/Memo

备注/Memo:
作者简介: 杨文杰( 1977- ) , 女, 山东泰安人, 博士, 主要研究方向: 计算机视觉, 数据挖掘, 图像处理, E-m a il: g reenflow er @ gm a il.com。
更新日期/Last Update: 2013-03-03