------------------------------------------------------------------------ -- | -- Module : Data.Datamining.Clustering.SOM -- Copyright : (c) Amy de Buitléir 2012-2013 -- License : BSD-style -- Maintainer : amy@nualeargais.ie -- Stability : experimental -- Portability : portable -- -- A Kohonen Self-organising Map (SOM). A SOM maps input patterns onto -- a regular grid (usually two-dimensional) where each node in the grid -- is a model of the input data, and does so using a method which -- ensures that any topological relationships within the input data are -- also represented in the grid. This implementation supports the use -- of non-numeric patterns. -- -- In layman's terms, a SOM can be useful when you you want to discover -- the underlying structure of some data. A tutorial is available at -- <https://github.com/mhwombat/som/wiki>. -- -- NOTES: -- -- * Version 5.0 fixed a bug in the @`decayingGaussian`@ function. If -- you use @`defaultSOM`@ (which uses this function), your SOM -- should now learn more quickly. -- -- * The @gaussian@ function has been removed because it is not as -- useful for SOMs as I originally thought. It was originally designed -- to be used as a factor in a learning function. However, in most -- cases the user will want to introduce a time decay into the -- exponent, rather than simply multiply by a factor. -- -- References: -- -- * Kohonen, T. (1982). Self-organized formation of topologically -- correct feature maps. Biological Cybernetics, 43 (1), 59–69. -- ------------------------------------------------------------------------ {-# LANGUAGE UnicodeSyntax #-} module Data.Datamining.Clustering.SOM ( -- * Construction SOM, defaultSOM, customSOM, decayingGaussian, -- * Deconstruction toGridMap, -- * Advanced control trainNeighbourhood, incrementCounter ) where import Data.Datamining.Clustering.SOMInternal (SOM, defaultSOM, customSOM, decayingGaussian, toGridMap, trainNeighbourhood, incrementCounter)