# hsgsom: An implementation of the GSOM clustering 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.

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Versions [faq] | 0.1.0, 0.2.0 |
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Dependencies | base, 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 2009-04-27T21:59:36Z |

Distributions | NixOS:0.2.0 |

Downloads | 1643 total (12 in the last 30 days) |

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## Modules

[Index]

## Downloads

- hsgsom-0.1.0.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)