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http://hdl.handle.net/UCSP/15891| Title: | A GH-SOM optimization with SOM labelling and dunn index |
| Authors: | Bokan Garay, Alessandro Ponce Contreras, Guillermo Patiño Escarcina, Raquel |
| Keywords: | Cluster validation;Clustering methods;Data sets;Growing hierarchical self-organizing maps;Unsupervised classification;Intelligent systems;Optimization |
| Issue Date: | 2011 |
| Publisher: | Scopus |
| metadata.dc.relation.uri: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856748692&doi=10.1109%2fHIS.2011.6122168&partnerID=40&md5=84d450a8bb887d678d92546afd361157 |
| Abstract: | Clustering is an unsupervised classification method that divides a data set in groups, where the elements of a group have similar characteristics to each other. A well-known clustering method is the Growing Hierarchical Self-Organizing Map (GH-SOM), that improves the results of an ordinary SOM by controlling the number of neurons generated. In this paper it is proposed a optimization of the typical GH-SOM, using a cluster validation index to verify the quality of partitioning. © 2011 IEEE. |
| URI: | http://repositorio.ucsp.edu.pe/handle/UCSP/15891 |
| ISBN: | 9781457721502 |
| Appears in Collections: | Artículos de investigación |
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