A ruby library which implements clustering and classification
algorithms for text mining.

Clustering algorithms currently implemented are - K-Means, and
Hierarchical clustering, LSI. Many variations of these algorithms are
also available, where you can change the similarity matrix, use
refined version, i.e., hierarchical/bisecting clustering followed by
Kmeans. 

LSI can be also used for clustering. In this first SVD transformation
is done, and then the documents in the new space are clustered (any of
KMeans, hierarchical/bisecting clustering can be used).

Hierarchical gives better results, but complexity roughly O(n*n)

K-means is very fast, O(k*n*i), i is number of iterations.

the examples need google/yahoo api keys, and the yahoo example requires 
ysearch-rb from

http://developer.yahoo.com/download/download.html

The multinomial, complement and weightnormalized complement Bayes
algorithm also implementd.

Lsi can also be used for classification.

Thanks a lot to several people who researched and worked on the several ideas.

Happy hacking...... 


ToDo: 
Add more documentation, and explain the API.
Add more examples.
Incorporate the C version of Gorrell and Simon Funk's GHA and SVD algorithm, also 
write a Ruby version.
Incorporate my own C version of Kernel SVD, and GHA.
Explore ways to improve and make a better tokenizer, and introduce other NLP techniques.
Add more classification algos: Decision Trees, and various extensions of it.
Ruby and C version of SVM and NN.
Feature Selection.