Discovering cluster-based local outliers

被引：605

作者：

He, ZY ^{[1
]}

Xu, XF ^{[1
]}

Deng, SC ^{[1
]}

机构：

[1] Harbin Inst Technol, Dept Comp Sci & Engn, Harbin 150001, Peoples R China

来源：

PATTERN RECOGNITION LETTERS | 2003年 / 24卷 / 9-10期

关键词：

outlier detection; clustering; data mining;

D O I：

10.1016/S0167-8655(03)00003-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present a new definition for outlier: cluster-based local outlier, which is meaningful and provides importance to the local data behavior. A measure for identifying the physical significance of an outlier is designed, which is called cluster-based local outlier factor (CBLOF). We also propose the FindCBLOF algorithm for discovering outliers. The experimental results show that our approach outperformed the existing methods on identifying meaningful and interesting outliers. (C) 2003 Elsevier Science B.V. All rights reserved.

引用

页码：1641 / 1650

页数：10

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