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Proceedings Paper

Mining maximal approximate numerical frequent patterns from uncertain data and application for emitter entity resolution
Author(s): Xin Xu
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Paper Abstract

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.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104431I (19 June 2017);
Show Author Affiliations
Xin Xu, NRIEE (China)

Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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