Mining Maximal Frequent Patterns With Similarity Matrices of Data Records

被引：0

作者：

Yuan, Hua ^{[1
]}

Wu, Junjie ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Dept Management Sci & E Commerce, Chengdu 610054, Peoples R China

[2] Beihang Univ, Sch Econ & Management, Beijing 100083, Peoples R China

来源：

ELECTRONIC-BUSINESS INTELLIGENCE: FOR CORPORATE COMPETITIVE ADVANTAGES IN THE AGE OF EMERGING TECHNOLOGIES & GLOBALIZATION | 2010年 / 14卷

基金：

中国国家自然科学基金;

关键词：

Data mining; Maximal frequent pattern; Similarity matrix;

D O I：

暂无

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

In this paper, we proposed a similarity matrix based method to mining maximal frequent patterns from large database. The study is very different from the previous Apriori-liked method. Especially, the method can be performed directly on the original data in database without various format transformation. The analyzing and experimental results show that the method is useful for frequent pattern mining tasks with large data set.

引用

页码：498 / +

页数：3

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