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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15912
Title: DB-GNG: A constructive self-organizing map based on density
Authors: Ocsa, Alexander
Bedregal, Carlos
Cuadros Vargas, Ernesto
Keywords: Artificial intelligence;Computer networks;Conformal mapping;Information services;Search engines;Joint conference;Self-organizing Maps;Neural networks
Issue Date: 2007
Publisher: Scopus
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-51749101515&doi=10.1109%2fIJCNN.2007.4371257&partnerID=40&md5=e681e70b25105b83958c4c34d37f2e15
Abstract: Nowadays applications require efficient and fast techniques due to the growing volume of data and its increasing complexity. Recent studies promote the use of Access Methods (AMs) with Self-Organizing Maps (SOMs) for a faster similarity information retrieval. This paper proposes a new constructive SOM based on density, which is also useful for clustering. Our algorithm creates new units based on density of data, producing a better representation of the data space with a less computational cost for a comparable accuracy. It also uses AMs to reduce considerably the Number of Distance Calculations during the training process, outperforming existing constructive SOMs by as much as 89%. ©2007 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15912
ISBN: urn:isbn:9781424413805
ISSN: 10987576
Appears in Collections:Artículos - Ciencia de la computación

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