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- W3124290834 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." @default.
- W3124290834 created "2021-02-01" @default.
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- W3124290834 date "1995-11-01" @default.
- W3124290834 modified "2023-09-27" @default.
- W3124290834 title "Self organizing maps for outlier detection" @default.
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