In this paper a Self-Organizing Map (SOM) robust to the presence of outliers, the Smoothed SOM (S-SOM), is proposed. S-SOM improves the properties of input density mapping, vector quantization, and clustering of the standard SOM in the presence of outliers by upgrading the learning rule in order to smooth the representation of outlying input vectors onto the map. The upgrade of the learning rule is based on the complementary exponential distance between the input vector and its closest codebook. The convergence of the S-SOM to a stable state is proved. Three comparative simulation studies and a suggestive application to digital innovation data show the robustness and effectiveness of the proposed S-SOM. Supplementary materials for this article are available.
|Titolo:||Smoothed Self-Organizing Map for robust clustering|
De Giovanni, Livia (Corresponding)
|Data di pubblicazione:||2020|
|Appare nelle tipologie:||01.1 - Articolo su rivista (Article)|
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|Information_Science_2019_12.pdf||Versione dell'editore||DRM non definito||Administrator|