一种基于邻接表的最大频繁项集挖掘算法

殷茗; 王文杰; 张煊宇; 姜继娇

doi:10.11999/JEIT180692

一种基于邻接表的最大频繁项集挖掘算法

doi: 10.11999/JEIT180692

1.
西北工业大学软件与微电子学院西安 710072
2.
西北工业大学管理学院西安 710072

基金项目: 教育部人文与社会科学基金(16YJA630068, 18YJA630043)，航空科学基金(2016ZG53071)，陕西省自然科学基础研究计划项目(2018JM7008)，陕西省社会科学基金(2018S28)，西北工业大学研究生种子基金(ZZ2018222)

详细信息

作者简介:
殷茗：女，1978年生，博士，副教授，主要研究方向为企业信息化、信息管理与信息系统、电子服务

王文杰：男，1992年生，硕士，主要研究方向为数据挖掘、机器学习

张煊宇：男，1995年生，硕士，主要研究方向为信息管理与信息系统

姜继娇：男，1979年生，博士，副教授，主要研究方向为行为金融与风险管理

通讯作者:
王文杰　wenjie@mail.nwpu.edu.cn

中图分类号: TP311.5
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- 文章访问数: 1976
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- PDF下载量: 79
- 被引次数: 0
出版历程
- 收稿日期: 2018-07-08
- 修回日期: 2019-05-17
- 网络出版日期: 2019-05-29
- 刊出日期: 2019-08-01

A Maximal Frequent Itemsets Mining Algorithm Based on Adjacency Table

1.
Institute of Software and Microelectronics, Northwestern Polytechnical University, Xi’an 710072, China
2.
Management School, Northwestern Polytechnical University, Xi’an 710072, China

Funds: Ministry of Education Humanities and Social Science Foundation (16YJA630068, 18YJA630043), Aeronautical Science Fund of China (2016ZG53071), Shaanxi Natural Science Basic Research Project (2018JM7008), Shaanxi Social Science Foundation Project (2018S28), Graduate Student Seed Fund Project of Northwestern Polytechnical University (ZZ2018222)

摘要

摘要: 针对Apriori算法与FP-Growth算法在最大频繁项集挖掘过程中存在的运行低效、内存消耗大、难以适应稠密数据集的处理、影响大数据价值挖掘时效等问题，该文提出一种基于邻接表的最大频繁项集挖掘算法。该算法只需遍历数据库一次，同时用哈希表对邻接表进行辅助存储，减小了遍历的空间规模。理论分析与实验结果表明，该算法时间与空间复杂度较低，提高了最大频繁项集挖掘速率，尤其在处理稠密数据集时具有较好的优越性。
- 数据挖掘 /
- 频繁项集 /
- Apriori /
- FP-Growth /
- FP-Tree
Abstract: To solve the problems of Apriori algorithm and FP-Growth algorithm in the process of mining the maximal frequent itemsets, which refer to inefficient operation, high memory consumption, difficulty in adapting to the process of dense datasets, and affecting the time-effectiveness of large data value mining, this paper proposes a maximal frequent itemsets mining algorithm based on adjacency table. The algorithm only needs to traverse the database once and adopts the hash table to store the adjacency table, which reduces the memory consumption. Theoretical analysis and experimental results show that the algorithm has lower time and space complexity and improves the mining rate of maximal frequent itemsets, especially when dealing with dense datasets.
- Data mining /
- Frequent itemsets /
- Apriori /
- FP-Growth /
- FP-Tree

HTML全文

图 1 顶头表与FP-Tree

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图 2 基于邻接表的最大频繁项集挖掘过程

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图 3 由数据集生成邻接表

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图 4 处理稀疏与稠密数据集不同事务数量的效率对比图

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图 5 处理稀疏与稠密数据集不同支持度计数的效率对比图

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表 1 事务数据库

TID	项
T100	B, C, E
T200	F, B
T300	C, A, D
T400	D, B, C, A, E
T500	C, E, D
T600	E, F

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表 2 3种算法的最大频繁项集挖掘结果

Apriori	FP-Growth	基于邻接表的算法	支持度
(A,C:2)	(A,C:2)	(C,A:2)	0.3
(D,A:2)	(A,D:2)	(A,D:2)	0.3
(B,C:2)	(B,C:2)	(B,C:2)	0.3
(E,B:2)	(B,E:2)	(B,E:2)	0.3
(D,C:3)	(D,C:3)	(C,D:3)	0.5
(C,E:3)	(E,C:3)	(C,E:3)	0.5
(D,E:2)	(D,E:2)	(E,D:2)	0.3
(D,A,C:2)	(A,C,D:2)	(C,A,D:2)	0.3
(E,C,B:2)	(B,C,E:2)	(B,C,E:2)	0.3
(D,C,E:2)	(D,C,E:2)	(C,D,E:2)	0.3

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