Apache Mahout 0.2: MapReduce LDA, Random Forests, and Frequent ItemSet Miner

Grant announced on the Lucid Imagination blog that Mahout 0.2 is released. Mahout is a library of scalable (distributed) machine learning algorithms using MapReduce.

Mahout 0.2 has several key new features that are worth taking a look at:

• Latent Dirichlet Allocation (LDA) (JIRA information) - LDA is a form bayesian topic modeling; a type of clustering that discovers hidden "topics" from a collection of documents. See the original LDA paper by Blei, et. al. and the Mallet LDA implementation from UMass (not MapReduce).
  
  
• K Nearest Neighbor (KNN) and Singular Value Decomposition (SVD) based recommender - JIRA. The implementation is based on a paper by the Netflix prize winning team: Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
  
  
• Random Forests -(JIRA) An ensemble classifier. See Breiman's description for background on Random Forests.
  
  
• Frequent Itemset Pattern Miner - (JIRA) - An algorithm that analyzes co-occurrence of items in a basket to suggest new items. This is an implementation of the Parallel FP Growth algorithm.

The release also has many other bug fixes and improvements. Keep up the good work guys!