  Self-organizing maps (SOM) are widely used for their topology preservation
property: neighboring input vectors are quantified (or classified) either on
the same location or on neighbor ones on a predefined grid. SOM are also widely
used for their more classical vector quantization property. We show in this
paper that using SOM instead of the more classical Simple Competitive Learning
(SCL) algorithm drastically increases the speed of convergence of the vector
quantization process. This fact is demonstrated through extensive simulations
on artificial and real examples, with specific SOM (fixed and decreasing
neighborhoods) and SCL algorithms.
