From the homepage:
Mahout currently has
- Collaborative Filtering
- User and Item based recommenders
- K-Means, Fuzzy K-Means clustering
- Mean Shift clustering
- Dirichlet process clustering
- Latent Dirichlet Allocation
- Singular value decomposition
- Parallel Frequent Pattern mining
- Complementary Naive Bayes classifier
- Random forest decision tree based classifier
- High performance java collections (previously colt collections)
- A vibrant community
- and many more cool stuff to come by this summer thanks to Google summer of code
If you are interested in Mahout, be sure to read the notes on future plans.
As the project moves towards a 1.0 release, the community is working to clean up and/or remove parts of the code base that are under-supported or that underperform as well as to better focus the energy and contributions on key algorithms that are proven to scale in production and have seen wide-spread adoption. To this end, in the next release, the project is planning on removing support for the following algorithms unless there is sustained support and improvement of them before the next release.
If you see an algorithm you need, best to step up or support someone else stepping up to support and improve the existing code.