= 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 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.