Mining Maximal Approximate Numerical Frequent Patterns from Uncertain Data and Application for Emitter Entity Resolution

被引：0
|
作者：
Xu, Xin [1 ]
机构：
[1] NRIEE, Sci & Technol Informat Syst Engn Lab, Nanjing 210007, Jiangsu, Peoples R China
来源：
SECOND INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION | 2017年 / 10443卷
基金：
中国国家自然科学基金;
关键词：
fuzzy pattern mining; numerical pattern mining; discretization; entity resolution; signal processing;
D O I：
10.1117/12.2280284
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Numerous fuzzy pattern mining methods have been proposed to address the uncertainty and incompleteness of quantitative data. Traditional fuzzy pattern mining methods generally have to transform the original quantitative values into either crystal items or fuzzy regions first, which is hard to apply without comprehensive domain knowledge. In addition, existing numerical pattern mining methods generally suffer high computational cost. Inspired by the above problems, we put forward an efficient maximal approximate numerical frequent pattern mining (MANFPM) method without fuzzy item or region specification. Experimental results have validated its scalability and effectiveness for application in emitter entity resolution.
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页数：5
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