| dc.date.accessioned | 2019-01-29T22:19:55Z | |
| dc.date.accessioned | 2023-05-30T23:27:49Z | |
| dc.date.available | 2019-01-29T22:19:55Z | |
| dc.date.available | 2023-05-30T23:27:49Z | |
| dc.date.created | 2019-01-29T22:19:55Z | |
| dc.date.issued | 2011 | |
| dc.identifier | urn:isbn:9781457721502 | |
| dc.identifier | http://repositorio.ucsp.edu.pe/handle/UCSP/15891 | |
| dc.identifier | https://doi.org/10.1109/HIS.2011.6122168 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/6477703 | |
| dc.description.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. | |
| dc.language | eng | |
| dc.publisher | Scopus | |
| dc.relation | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856748692&doi=10.1109%2fHIS.2011.6122168&partnerID=40&md5=84d450a8bb887d678d92546afd361157 | |
| dc.rights | info:eu-repo/semantics/restrictedAccess | |
| dc.source | Repositorio Institucional - UCSP | |
| dc.source | Universidad Católica San Pablo | |
| dc.source | Scopus | |
| dc.subject | Cluster validation | |
| dc.subject | Clustering methods | |
| dc.subject | Data sets | |
| dc.subject | Growing hierarchical self-organizing maps | |
| dc.subject | Unsupervised classification | |
| dc.subject | Intelligent systems | |
| dc.subject | Optimization | |
| dc.title | A GH-SOM optimization with SOM labelling and dunn index | |
| dc.type | info:eu-repo/semantics/conferenceObject | |
