计算机工程与应用2012，Vol.48Issue(4)：118-120,131,4.DOI:10.3778/j.issn.1002-8331.2012.04.035
基于知识粒度的异常数据挖掘算法
Outlier mining algorithm based on knowledge granulation
摘要
Abstract
Granular computing theory is a new efficient method to deal with uncertain, incomplete and inconsistent knowledge. Knowledge granulation is one of important tools to deal with uncertain information in granular computing theory. Many existing algorithms of outlier mining mainly aim for certain data, very little work has been done for uncertain data aiming to outlier mining based on knowledge granulation. Therefore, after introducing knowledge granulation concept, relative knowledge granulation and outlier degree are defined for measuring the outlier data. A new algorithm for outlier mining based on knowledge granulation is proposed. This algorithm can effectively obtain outliers from data set. The validity of the algorithm is depicted by an example.关键词
粒计算/粗糙集/异常数据挖掘/知识粒度Key words
granular computing/ rough sets/ outlier data mining/ knowledge granulation分类
信息技术与安全科学引用本文复制引用
陈玉明,吴克寿,孙金华..基于知识粒度的异常数据挖掘算法[J].计算机工程与应用,2012,48(4):118-120,131,4.基金项目
国家自然科学基金(No.60903203). （No.60903203）
