Som-Elm-Self-Organized Clustering using Elm
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.
Recommended Citation
Y. Miche et al., "Som-Elm-Self-Organized Clustering using Elm," Neurocomputing, vol. 165, pp. 238 - 254, Elsevier, Oct 2015.
The definitive version is available at https://doi.org/10.1016/j.neucom.2015.03.014
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Clustering; ELM; Self-Organized; SOM
International Standard Serial Number (ISSN)
1872-8286; 0925-2312
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Oct 2015
