A betterment algorithm for mining association rules

被引：0

作者：

Dai Yue-ming ^{[1
]}

Zhu Xi-jun ^{[1
]}

机构：

[1] Qingdao Univ Technol, Dept Math & Phys, Qingdao 266033, Peoples R China

来源：

PROCEEDINGS OF 2005 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1 AND 2 | 2005年

关键词：

collective strength; confidence; association rules; itemsets;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Some achievement in the study of mining association rule is summarized, such as mining association rules based on the framework of collective and confidence. The algorithm overcomes some shortcoming of traditional algorithms, such as spuriousness in itemset generation including phenomenon of negatively association, and the inefficiency in dealing with dense data sets.

引用

页码：1397 / 1399

页数：3

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