Nearest prototype classification of special school families based on hierarchical compact sets clustering

Yenny Villuendas-Rey, Carmen Rey-Benguría, Yailé Caballero-Mota, María Matilde García-Lorenzo

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1 Cita (Scopus)

Resumen

The family orientation process in Cuban Schools for children with Affective - Behavioral Maladies (SABM) involves clustering and classification of mixed type data with non-symmetric similarity functions. To improve this process, this paper includes some novel characteristics in clustering and prototype selection. The proposed approach uses a hierarchical clustering based on compact sets, making it suitable for dealing with non-symmetric similarity functions, as well as with mixed and incomplete data. The proposal obtains very good results on the SABM data, and over repository databases. In addition, the proposed clustering method is able to detect the true partitions of data and it was significantly better with respect to others according to external validity indexes. In prototype selection, the proposal obtains a highly reduced prototype set, while maintains the original classifier accuracy.

Idioma originalInglés
Título de la publicación alojadaAdvances in Artificial Intelligence, IBERAMIA 2012 - 13th Ibero-American Conference on AI, Proceedings
EditorialSpringer Verlag
Páginas662-671
Número de páginas10
ISBN (versión impresa)9783642346538
DOI
EstadoPublicada - 2012
Publicado de forma externa
Evento13th Ibero-American Conference on Advancesin Artificial Intelligence, IBERAMIA 2012 - Cartagena de Indias, Colombia
Duración: 13 nov. 201216 nov. 2012

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen7637 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia13th Ibero-American Conference on Advancesin Artificial Intelligence, IBERAMIA 2012
País/TerritorioColombia
CiudadCartagena de Indias
Período13/11/1216/11/12

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