计算机应用研究2011，Vol.28Issue(7)：2553-2557,5.DOI:10.3969/j.issn.1001-3695.2011.07.043
基于流程挖掘的甄别正常与非正常案例的模型
Detection model of normal and abnormal instances based on process-mining
徐晓蕾 1张立群 2刘镔 1李栋2
作者信息
- 1. 山东大学计算机学院,济南250061
- 2. 济南军区自动化站,济南250022
- 折叠
摘要
Abstract
Now there are lots of studies on detecting of abnormal cases. They are mostly studied how to build distinguish standard or feature library artificially. This kind of method not only make preparations troublesome, but also limit the later discriminating ability and low accuracy. This paper proposed a detection model of normal and abnormal instances( DMNAI) based on process-mining, throughing frequent patterns to extract the case feature. Used the neural network classifier to detect cases, and constructed a automatically detection model. Consequently, it could void a manually set standards subjectively. Experiments show that DMNAI after real data validation, can effectively discriminate abnormal cases.关键词
数据挖掘/结构模式/频繁模式/流程挖掘/分类模型Key words
data mining/ structure pattern/ frequent pattern/ flow mining/ classification model分类
信息技术与安全科学引用本文复制引用
徐晓蕾,张立群,刘镔,李栋..基于流程挖掘的甄别正常与非正常案例的模型[J].计算机应用研究,2011,28(7):2553-2557,5.
