基于属性约简和相对熵的离群点检测算法

山东大学学报(工学版) ›› 2011, Vol. 41 ›› Issue (6): 31-36.

基于属性约简和相对熵的离群点检测算法

胡云1,2,李慧1,施珺1,蔡虹1

1.淮海工学院计算机工程学院, 江苏连云港 222000; 2.南京大学计算机科学与技术系, 江苏南京 210000

收稿日期:2011-04-15 出版日期:2011-12-16 发布日期:2011-04-15
作者简介:胡云(1977- ),女,江苏连云港人,讲师,博士研究生,主要研究方向为数据挖掘,智能信息处理. E-mail: huyunzhang@yahoo.com.cn
基金资助:
江苏省自然科学基金资助项目(BK2008190)

An outlier detection algorithm based on attribute reduction and relative entropy

HU Yun1,2, LI Hui1, SHI Jun1, CAI Hong1

1. School of Computer Engineering, Huaihai Institute of Technology, Lianyungang 222000, China;
2. Department of Computer Science and Technology, Nanjing University, Nanjing 210000, China

Received:2011-04-15 Online:2011-12-16 Published:2011-04-15

摘要/Abstract

摘要：

本研究结合信息熵与粗糙集理论中的属性约简技术,提出了一种新颖的离群点检测算法。这种方法通过在更小的属性子空间去获得相同或相近的离群数据集,使对离群数据的分析更加集中于较小的目标域。该算法对原属性空间进行划分,通过分析计算将具有最大相对熵与负相对势的对象集合判定为离群点集合。为了验证算法的有效性,还在通用数据集上进行了测试,理论分析和实验结果表明该离群点检测算法是有效可行的。

关键词: 属性简约, 相对熵, 离群点检测

Abstract:

A new outlier detection algorithm combining a rough set and information entropy technology was proposed. This approach could obtain similar outlier sets by means of searching in an attributes subspace, which could lead the analysis of outlier detection to focus better on narrow and specific object fields. This algorithm divided the original attribute space into several segments, which filtered out those subjects with largest relative entropy negative relative cardinality as the outliers. To prove this algorithm’s effectiveness, experiments on a real world dataset were conducted. Theoretical analysis and experimental results showed that this method of outlier detection was efficient and effective.

Key words: attribute deduction, relative entropy, outlier detection

中图分类号:

TP391

胡云1,2,李慧1,施珺1,蔡虹1. 基于属性约简和相对熵的离群点检测算法[J]. 山东大学学报(工学版), 2011, 41(6): 31-36.

HU Yun1,2, LI Hui1, SHI Jun1, CAI Hong1. An outlier detection algorithm based on attribute reduction and relative entropy[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2011, 41(6): 31-36.

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