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hsgsom: An implementation of the GSOM clustering algorithm.

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The growing self organising map (GSOM) algorithm is a clustering algorithm working on a set of n-dimensional numeric input vectors. It's output is a network of nodes laid out in two dimensions where each node has a weight vector associated with it. This weight vector has the same dimension as the input vectors and is meant to be intepreted as a cluster center, i.e. it represents those input vectors whose distance to the node's weight vector is minimal when compared to the distance to the other nodes weight vectors. See http://en.wikipedia.org/wiki/GSOM for an explanation of the algorithm. The algorithm was introduced in: Alahakoon, D., Halgamuge, S. K. and Sirinivasan, B. (2000) Dynamic Self Organizing Maps With Controlled Growth for Knowledge Discovery, IEEE Transactions on Neural Networks, Special Issue on Knowledge Discovery and Data Mining, 11, pp 601-614.

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Versions [RSS] 0.1.0, 0.2.0
Dependencies base (>=3 && <5), containers, random, stm, time [details]
License BSD-3-Clause
Author Stephan Günther
Maintainer Stephan Günther <gnn dot github at gmail dot com>
Category Data Mining, Clustering
Uploaded by StephanGuenther at 2011-02-10T01:15:54Z
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Reverse Dependencies 1 direct, 0 indirect [details]
Downloads 2001 total (3 in the last 30 days)
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Readme for hsgsom-0.2.0

[back to package description] 
= README

This is the README file of hsgsom, a haskell library implementing the
growing self organising map clustering algorithm.

== The Algorithm

The growing self organising map (GSOM) algorithm is a clustering algorithm
working on a set of n-dimensional numeric input vectors. It's output is a
network of nodes laid out in two dimensions where each node has a weight
vector associated with it. This weight vector has the same dimension as the
input vectors and is meant to be intepreted as a cluster center, i.e. it
represents those input vectors whose distance to the node's weight vector
is minimal when compared to the distance to the other nodes weight vectors.
See <http://en.wikipedia.org/wiki/GSOM> for an explanation of the algorithm.
The algorithm was introduced in:

  Alahakoon, D., Halgamuge, S. K. and Sirinivasan, B. (2000)
  Dynamic Self Organizing Maps With Controlled Growth
  for Knowledge Discovery,
  IEEE Transactions on Neural Networks,
  Special Issue on Knowledge Discovery and Data Mining, 11, pp 601-614.

== License

This package and its contents are licensed under the BSD 3 clause license.
You should have received a file called LICENSE containing said license
along with this package.

== Versioning

This README correponds to version 0.1.0 of hsgsom, so as you can see it is
a very early version.
Version numbers follow the pattern X.Y.Z and have the following meaning:

  - a change in Z corresponds to minor changes as in documentation changes
    or changes to the underlying implementation

  - a change in Y correponds to added functionality and/or backwards
    compatible interface changes/additions.

  - a change in X correpsonds to a major implementation change either
    drastically changing the algorithm behaviour or performance or
    changing the interface in a possibly not backwards compatible way.

== Questions, Bugs, etc...

If you think you have found a bug, or you have questions or suggestions
or really anything to say about the package it would be greatly appreciated
if you would drop me a note or an email.
This is my very firt attempt at packaging and releasing a substantial amount
my own code to the public and I'm eager to learn how to do thinks better.

Thanks for using/looking at this package and have a nice day.