Name: hsgsom
Version: 0.2.0
Cabal-Version: >= 1.6.0.3
Build-Type: Simple
License: BSD3
License-File: LICENSE
Data-Files: README
Author: Stephan Günther
Maintainer: Stephan Günther
Category: Data Mining, Clustering
Synopsis: An implementation of the GSOM clustering algorithm.
Description:
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 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.
Library
Build-Depends: base >= 3 && < 5, containers, random, time, stm
Exposed-Modules: Data.Datamining.Clustering.Gsom,
Data.Datamining.Clustering.Gsom.Coordinates,
Data.Datamining.Clustering.Gsom.Input,
Data.Datamining.Clustering.Gsom.Lattice,
Data.Datamining.Clustering.Gsom.Node,
Data.Datamining.Clustering.Gsom.Parallel,
Data.Datamining.Clustering.Gsom.Phase
GHC-Options: -O2 -fvia-C -optc-O3