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|
|Dependencies||base (>=3 && <5), containers, random, stm, time [details]|
|Maintainer||Stephan Günther <gnn dot github at gmail dot com>|
|Category||Data Mining, Clustering|
|Uploaded||by StephanGuenther at Thu Feb 10 01:15:54 UTC 2011|
|Downloads||1451 total (80 in the last 30 days)|
|Rating||(no votes yet) [estimated by Bayesian average]|
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