Self organizing maps for outlier detection

Alberto Muñoz and Jorge Muruzábal

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: 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 ofthe trained map (via Sammon's projection). 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.

Keywords: Self-organization; Atypical; Data; Robustness; Dimensionality; Reduction; Nonlinear; Projections (search for similar items in EconPapers)
Date: 1995-11
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:10345

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