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

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)

Abstract

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.
Original languageEnglish
Peer-reviewed scientific journalNeurocomputing
Volume165
Issue numberOctober
Pages (from-to)238-254
Number of pages17
ISSN0925-2312
DOIs
Publication statusPublished - 13.03.2015
MoE publication typeA1 Journal article - refereed

Keywords

  • 512 Business and Management
  • ELM
  • Self-organized
  • SOM
  • Clustering

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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