k-means clustering algorithm in Haskell
kmeans-vector is a Haskell library for performing the k-means clustering algorithm. It is based on the very efficient 'vector' library.
You can find examples in the
Feel free to contribute, may it be features, performance improvements, etc.
Performances (0.3 version)
The library has completely been rewritten and beats the 0.2 or the
hands down. I also plan to work on an implementation of the Streaming k-means technique soon, but that'll be for the next version.
Note that this version also features some improvements in the usability.
Performances (0.2 version)
In addition to contributing a feature, Ville Tirronen also claimed to make the library about 50% faster, which was confirmed after running the algorithm on a dataset I had lying around.
Performances (0.1 version)
kmeans-vector is much faster than the existing kmeans package. For example, the kmeans package performs k-means on 10000 2D points with k=5 in 21.099s while kmeans-vector does the same in 6.605s. For 50000 3D points, with k=5 still, kmeans-vector performs in 47.853s while I Ctrl+C'd the kmeans version after 6 minutes and a half.
This library is written and maintained by Alp Mestanogullari, email@example.com. Ville Tirronen contributed code to the 0.2 version.