刘晓冰

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:德国多德蒙特大学

学位:博士

所在单位:运营与物流管理研究所

学科:企业管理

办公地点:大连理工大学经济管理学院

联系方式:13904286410(因年龄原因,停止招生)

电子邮箱:xbliu@dlut.edu.cn

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An improved association rules mining method

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论文类型:期刊论文

发表时间:2012-01-01

发表刊物:EXPERT SYSTEMS WITH APPLICATIONS

收录刊物:SCIE、EI

卷号:39

期号:1

页面范围:1362-1374

ISSN号:0957-4174

关键字:Association rule; Maximal frequent itemsets; Directed itemsets graph; Trifurcate linked list storage structure; Mining algorithm

摘要:Mining maximal frequent itemsets is of paramount relevance in many of data mining applications. The "traditional" algorithms address this problem through scanning databases many times. The latest research has already focused on reducing the number of scanning times of databases and then decreasing the number of accessing times of I/O resources in order to improve the overall mining efficiency of maximal frequent itemsets of association rules. In this paper, we present a form of the directed itemsets graph to store the information of frequent itemsets of transaction databases, and give the trifurcate linked list storage structure of directed itemsets graph. Furthermore, we develop the mining algorithm of maximal frequent itemsets based on this structure. As a result, one realizes scanning a database only once, and improves storage efficiency of data structure and time efficiency of mining algorithm. (C) 2011 Elsevier Ltd. All rights reserved.