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一种基于混合属性数据集的异常检测方法 被引量：1

An Anomaly Detection Method Based on Mixed Attribute Dataset
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摘要 针对混合属性数据集对象间差异性度量丢失原有数据特性的问题,引入了新的差异性度量方法,构造出对象的混合属性异常因子。在此基础上提出一种新的基于混合属性数据集的局部密度异常检测算法。实验结果表明,该算法高效可行,检测精度高,且参数设置简单。 For diversity measure between the mixed attribute dataset objects lost the original data characteris- tics, a new diversity measure method is introduced, the object mixed attribute outlier factor is constructed. And a new anomaly detection algorithm is presented based on mixed attribute dataset local density. The experimental re- sults show that the algorithm is feasible, efficient, high detection precision, and simple parameter set.
作者 唐永红 刘绪栋
机构地区 中国石油大学(华东)
出处 《科学技术与工程》 北大核心 2013年第7期1832-1835,1859,共5页 Science Technology and Engineering
基金 山东省自然科学基金(ZR2012DM010)资助
关键词 混合属性异常检测 差异度量 混合属性异常因子 局部密度 mixed attribute anomaly detection difference metrics mixed attribute outlier factor local density
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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参考文献10

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二级参考文献37

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科学技术与工程
2013年 第7期

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