DB-GNG: A constructive Self-Organizing Map based on densilty
Alexander Ocsa, Carlos Bedregal, Ernesto Cuadros-Vargas · 2007
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%.