Reduction Relaxation in Privacy Preserving Association Rules Mining

被引：0
|
作者：
Andruszkiewicz, Piotr [1 ]
机构：
[1] Warsaw Univ Technol, Inst Comp Sci, PL-00661 Warsaw, Poland
来源：
ADVANCES IN DATABASES AND INFORMATION SYSTEMS | 2013年 / 186卷
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D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In Privacy Preserving Association Rules Mining, when frequent sets are discovered, the relaxation can be used to decrease the false negative error component and, in consequence, to decrease the number of true frequent itemsets that are missed. We introduce the new type of relaxation - the reduction relaxation that enable a miner to decrease and control the false negative error for different lengths of frequent itemsets.
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页码：1 / 8
页数：8
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