Getting started with Mahout on DeveloperWorks

Grant Ingersoll has an article, Introducing Apache Mahout, published on IBM Developer works.

The article gives an introduction to different ML tasks and Mahouts implementations. Mahout current has the Taste recommendation system developed by Sean Owen. Clustering implementations including k-Means, fuzzy k-Means, Canopy, Dirichlet, and Mean-Shift. A Naive Bayes text classifier.

Grant covers the basics of getting these working in the article.

At the end he comments on what's next for Mahout:

On the immediate horizon are Map-Reduce implementations of random decision forests for classification, association rules, Latent Dirichlet Allocation for identifying topics in documents, and more categorization options using HBase and other backing storage options...