An efficient algorithm for mining large item sets

被引：0
|
作者：
Zheng, Hong-Zhen [1 ]
Chu, Dian-Hui [1 ]
Zhan, De-Chen [2 ]
机构：
[1] Harbin Inst Technol, Coll Comp Sci & Technol, Weihai 264209, Peoples R China
[2] Harbin Inst Technol, Coll Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China
来源：
3RD INT CONF ON CYBERNETICS AND INFORMATION TECHNOLOGIES, SYSTEMS, AND APPLICAT/4TH INT CONF ON COMPUTING, COMMUNICATIONS AND CONTROL TECHNOLOGIES, VOL 2 | 2006年
关键词：
data mining; association rules;
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：
It propose Online Mining Algorithm ( OMA) which online discover large item sets. Without pre-setting a default threshold, the OMA algorithm achieves its efficiency and threshold-flexibility by calculating itemsets' counts. It is unnecessary and independent of the default threshold and can flexibly adapt to any user's input threshold. In addition, we propose Cluster-Based Association Rule Algorithm (CARA) creates cluster tables to aid discovery of large item sets. It only requires a single scan of the database, followed by contrasts with the partial cluster tables. It not only prunes considerable amounts of data reducing the time needed to perform data scans and requiring less contrast, but also ensures the correctness of the mined results. By using the CARA algorithm to create cluster tables in advance, each CPU can be utilized to process a cluster table; thus large item sets can be immediately mined even when the database is very large.
引用
收藏
页码：151 / +
页数：2
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