Mining maximal approximate numerical frequent patterns from uncertain data and application for emitter entity resolution

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 <i>(MANFPM)</i> method without fuzzy item or region specification. Experimental results have validated its scalability and effectiveness for application in emitter entity resolution.