DB-GNG: A constructive Self-Organizing Map based on densilty

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%.