Self-organizing maps for outlier detection

被引：61

作者：

Munoz, A ^{[1
]}

Muruzabal, J ^{[1
]}

机构：

[1] Univ Carlos III Madrid, Dept Stat & Econometr, E-28903 Getafe, Spain

来源：

NEUROCOMPUTING | 1998年 / 18卷 / 1-3期

关键词：

self-organization; atypical data; robustness; dimensionality reduction; nonlinear projections;

D O I：

10.1016/S0925-2312(97)00068-4

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 address the problem of multivariate outlier detection using the (unsupervised) self-organizing map (SOM) algorithm introduced by Kohonen. We examine a number of techniques, based on summary statistics and graphics derived from the trained SOM, and conclude that they work well in cooperation with each other. Useful tools include the median interneuron distance matrix and the projection of the trained map (via Sammon's mapping). SOM quantization errors provide an important complementary source of information for certain type of outlying behavior. Empirical results are reported on both artificial and real data. (C) 1998 Elsevier Science B.V. All rights reserved.

引用

页码：33 / 60

页数：28

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