Apache Mahout

Fth-Hokage

Apache Mahout是一个机器学习的框架，构建在hadoop上支持大规模数据集的处理，目前最新版本0.4。

   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
  

Highlights of it:

• Model refactoring and CLI changes to improve integration and consistency
• New ClusterEvaluator and CDbwClusterEvaluator offer new ways to evaluate clustering effectiveness
• New Spectral Clustering and MinHash Clustering (still experimental)
• New VectorModelClassifier allows any set of clusters to be used for classification
  
• Map/Reduce job to compute the pairwise similarities of the rows of a matrix using a customizable similarity measure
• Map/Reduce job to compute the item-item-similarities for item-based collaborative filtering
• RecommenderJob has been evolved to a fully distributed item-based recommender
• Distributed Lanczos SVD implementation
• More support for distributed operations on very large matrices
• Easier access to Mahout operations via the command line
• New HMM based sequence classification from GSoC (currently as sequential version only and still experimental)
• Sequential logistic regression training framework
• New SGD classifier
• Experimental new type of NB classifier, and feature reduction options for existing one
• New vector encoding framework for high speed vectorization without a pre-built dictionary
• Additional elements of supervised model evaluation framework
• Promoted several pieces of old Colt framework to tested status (QR decomposition, in particular)
• Can now save random forests and use it to classify new data
  

第五条就是我尝试做又没成功的地方啊

zouquan

赞，有成功的代码么？发一个上来，看看

xmubingo

先大力搞hadoop把...机器什么都到位了。:D