An Efficient Algorithm for Mining Large Item Sets

被引：1
|
作者：
Zheng, Hong-Zhen [1 ]
Chu, Dian-Hui [1 ]
Zhan, De-Chen [1 ]
Xu, Xiao-Fei [1 ]
机构：
[1] Harbin Inst Technol, Coll Comp Sci & Technol, Weihai 264209, Peoples R China
来源：
FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 2, PROCEEDINGS | 2008年
关键词：
Large item sets; Data mining; Association rules;
D O I：
10.1109/FSKD.2008.679
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
It propose Online Mining Algorithm (OMA) which online discover large item sets. Without presetting a default threshold, the OMA algorithm achieves its efficiency and threshold-flexibility by calculating item-sets' 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：561 / 564
页数：4
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