selforgmap

Self-organizing map

Syntax

selforgmap(dimensions,coverSteps,initNeighbor,topologyFcn,distanceFcn)

Description

Self-organizing maps learn to cluster data based on similarity, topology, with a preference (but no guarantee) of assigning the same number of instances to each class.

Self-organizing maps are used both to cluster data and to reduce the dimensionality of data. They are inspired by the sensory and motor mappings in the mammal brain, which also appear to automatically organizing information topologically.

selforgmap(dimensions,coverSteps,initNeighbor,topologyFcn,distanceFcn) takes these arguments,

dimensions

Row vector of dimension sizes (default = [8 8])

coverSteps

Number of training steps for initial covering of the input space (default = 100)

initNeighbor

Initial neighborhood size (default = 3)

topologyFcn

Layer topology function (default = 'hextop')

distanceFcn

Neuron distance function (default = 'linkdist')

and returns a self-organizing map.

Examples

Use Self-Organizing Map to Cluster Data

Here a self-organizing map is used to cluster a simple set of data.

x = simplecluster_dataset;
net = selforgmap([8 8]);
net = train(net,x);
view(net)
y = net(x);
classes = vec2ind(y);

See Also

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Introduced in R2010b

Deep Learning Toolbox Documentation

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