hsgsom: An implementation of the GSOM clustering algorithm.

[ bsd3, clustering, data-mining, library ] [ Propose Tags ]

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 base, containers, random, stm, time [details] BSD-3-Clause Stephan Günther Stephan Günther Data Mining, Clustering by StephanGuenther at 2009-04-27T21:59:36Z 1 direct, 0 indirect [details] 1952 total (8 in the last 30 days) (no votes yet) [estimated by Bayesian average] λ λ λ Docs uploaded by userBuild status unknown

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= 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.

This package and its contents are licensed under the BSD 3 clause 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

- 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.