Mining of maximum frequent itemsets and frequent closed itemsets based on frequent itemsets

doi:10.3778/j.issn.1002-8331.2008.28.049

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (28): 146-148.DOI: 10.3778/j.issn.1002-8331.2008.28.049

• 数据库、信号与信息处理 • Previous Articles Next Articles

Mining of maximum frequent itemsets and frequent closed itemsets based on frequent itemsets

JIANG Han¹,JIA Jiong²,XU Feng¹

1.Department of Computer Engineering，Jining Vocational & Technical College，Jining，Shandong 272000，China
2.College of Mathematics，Physics and Information Engineering，Zhejiang Normal University，Jinhua，Zhejiang 321004，China

Received:2007-11-21 Revised:2008-03-04 Online:2008-10-01 Published:2008-10-01
Contact: JIANG Han

基于频繁项集挖掘最大频繁项集和频繁闭项集

姜晗¹,贾泂²,徐峰¹

1.济宁职业技术学院计算机工程系，山东济宁 272000
2.浙江师范大学数理与信息工程学院，浙江金华 321004

通讯作者: 姜晗

Abstract

Abstract: In this paper，a new kind of algorithms BFI-DMFI（Mining Maximum Frequent Itemsets） and BFI-DCFI（Mining Frequent Closed Itemsets） is proposed.In BFI-DMFI，we can confirm whether a frequent itemsets is also a maximum frequent itemsets through detecting whether exiting their superset itemsets in frequent itemsets.In BFI-DCFI，in order to generate frequent closed itemsets，we can make use of mining maximum frequent itemsets from frequent itemsets which have equal support.This kind of algorithms provides a new method for reducing the set of association rules.

Key words: maximum frequent itemsets, frequent closed itemsets, frequent itemsets, association rules

摘要： 提出了基于频繁项集的最大频繁项集（BFI-DMFI）和频繁闭项集挖掘算法（BFI-DCFI）。BFI-DMFI算法通过逐个检测频繁项集在其集合中是否存在超集确定该项集是不是最大频繁项集；BFI-DCFI算法则是通过挖掘所有支持度相等的频繁项集中的最大频繁项集组合生成频繁闭项集。该类算法的提出，为关联规则的精简提供了一种新的解决方法。

关键词: 最大频繁项集, 频繁闭项集, 频繁项集, 关联规则

JIANG Han¹,JIA Jiong²,XU Feng¹. Mining of maximum frequent itemsets and frequent closed itemsets based on frequent itemsets[J]. Computer Engineering and Applications, 2008, 44(28): 146-148.

姜晗¹,贾泂²,徐峰¹. 基于频繁项集挖掘最大频繁项集和频繁闭项集[J]. 计算机工程与应用, 2008, 44(28): 146-148.

[1]	ZHANG Zhenhai，ZHANG Xiangting. Context-Aware Information Service Recommendation Method for High-Speed Rail [J]. Computer Engineering and Applications, 2021, 57(12): 231-236.
[2]	YANG Geying, SHEN Xiajiong, SHI Xianjin, ZHANG Lei. Visualization of Association Rules in Context of Concept Lattices [J]. Computer Engineering and Applications, 2021, 57(1): 84-91.
[3]	JI Wenlu, WANG Hailong, SU Guibin, LIU Lin. Review of Recommendation Methods Based on Association Rules Algorithm [J]. Computer Engineering and Applications, 2020, 56(22): 33-41.
[4]	GU Junhua, SU Ming, ZHANG Yajuan, ZHANG Danhong. Research on Fast Frequent Pattern Mining Algorithm Based on Bitmap-Code List [J]. Computer Engineering and Applications, 2020, 56(19): 86-93.
[5]	LIU Yan, ZHANG Jin, CHEN Jing, YIN Meijuan, ZHANG Weili. Detection of hype groups based on mining maximum frequent itemsets in Microblogs [J]. Computer Engineering and Applications, 2017, 53(4): 90-97.
[6]	XUE Fuliang1, MA Li2. Improved association rule recommendation method based on dynamic product taxonomy [J]. Computer Engineering and Applications, 2016, 52(4): 135-141.
[7]	SHANG Zhaowei, LI Zhenhua, ZHANG Lan. Log-based collaborative automatic image annotation [J]. Computer Engineering and Applications, 2015, 51(8): 178-182.
[8]	ZHANG Zhonglin, SHI Haoyin, YAN Guanghui. Method of tendency measure mining in dynamic association rules based on grey-Markov model [J]. Computer Engineering and Applications, 2015, 51(7): 154-159.
[9]	LIU Weiming1，2, KUAI Hailong1, CHEN Zhigang3, MAO Yimin1，4. Algorithm based on ordered tree for mining maximal frequent items from uncertain data [J]. Computer Engineering and Applications, 2015, 51(24): 145-149.
[10]	YIN Shaohong, SHAN Kunyu, FAN Guidan. Mining algorithm research of data stream maximum frequent itemsets in sliding window [J]. Computer Engineering and Applications, 2015, 51(22): 145-149.
[11]	DING Bangxu, HUANG Yongqing. Algorithm of matrix and Prefix-tree for mining frequent itemsets [J]. Computer Engineering and Applications, 2015, 51(22): 154-157.
[12]	TANG Wei. Efficient formal method for mining fuzzy association rules [J]. Computer Engineering and Applications, 2015, 51(18): 114-119.
[13]	LIU Fengbin1, YUAN Zhiyong1, XIAO Ling2, WANG Huiling2, WANG Gaohua2. Anxiety and depression factors mining based on improved BUS algorithm [J]. Computer Engineering and Applications, 2015, 51(18): 126-130.
[14]	MA Lei1, JIA Qinan2, ZHANG Jun2, BAO Yuanyuan1, HE Jianfeng1, LI Long3. Study of auxiliary diagnostic expert system based on combined genetic patterns mining [J]. Computer Engineering and Applications, 2014, 50(24): 122-126.
[15]	ZHAO Jiaojiao, ZHAO Shuliang, GUO Xiaobo, LIU Jundan. Visualization method of association rules based on natural language generation [J]. Computer Engineering and Applications, 2014, 50(23): 122-126.

Mining of maximum frequent itemsets and frequent closed itemsets based on frequent itemsets

基于频繁项集挖掘最大频繁项集和频繁闭项集

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics