SOM-ELM—Self-Organized Clustering using ELM

Yoan Miche, Anton Akusok, David Veganzones, Kaj-Mikael Björk, Eric Séverin, Philippe du Jardin, Maite Termenon, Amaury Lendasse

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

12 Citeringar (Scopus)

Sammanfattning

This paper presents two new clustering techniques based on Extreme Learning Machine (ELM). These clustering techniques can incorporate a priori knowledge (of an expert) to define the optimal structure for the clusters, i.e. the number of points in each cluster. Using ELM, the first proposed clustering problem formulation can be rewritten as a Traveling Salesman Problem and solved by a heuristic optimization method. The second proposed clustering problem formulation includes both a priori knowledge and a self-organization based on a predefined map (or string). The clustering methods are successfully tested on 5 toy examples and 2 real datasets.
OriginalspråkEngelska
Referentgranskad vetenskaplig tidskriftNeurocomputing
Volym165
UtgåvaOctober
Sidor (från-till)238-254
Antal sidor17
ISSN0925-2312
DOI
StatusPublicerad - 13.03.2015
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

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    Miche, Y., Akusok, A., Veganzones, D., Björk, K-M., Séverin, E., du Jardin, P., Termenon, M., & Lendasse, A. (2015). SOM-ELM—Self-Organized Clustering using ELM. Neurocomputing, 165(October), 238-254. https://doi.org/10.1016/j.neucom.2015.03.014