Project details for Apache Mahout

Logo Apache Mahout 0.4

by gsingers - November 2, 2010, 04:28:34 CET [ Project Homepage BibTeX Download ]

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Apache Mahout is an Apache Software Foundation project with the goal of creating both a community of users and a scalable, Java-based framework consisting of many machine learning algorithm implementations. The project currently has map-reduce enabled (via Apache Hadoop) implementations of several clustering algorithms (k-Means, Mean-Shift, Fuzzy k-Means, Dirichlet, Canopy), Naïve Bayes and Complementary Naïve Bayes classifiers, Hidden Markov Models, Stochastic Gradient Descent, Latent Dirichlet Allocation, Frequent Patternset Mining, Random Decision Forests, distributed Singular Value Decomposition, distributed collocations, collaborative filtering, as well as support for distributed evolutionary computing. We are also planning implementations of neural nets, expectation maximization, hierarchical clustering, Support Vector Machines, regression techniques, and Principal Component Analysis, amongst others.

Changes to previous version:

We are pleased to announce release 0.4 of Mahout. Virtually every corner of the project has changed, and significantly, since 0.3. Developers are invited to use and depend on version 0.4 even as yet more change is to be expected before the next release. Highlights include:

* 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
* Many, many small fixes, improvements, refactorings and cleanup
BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Arff, Lucene, Mahout Vector
Tags: Classification, Clustering, K Nearest Neighbor Classification, Genetic Algorithms, Collaborative Filtering, Collocations, Frequent Pattern Mining, Scalable Singular Value Decomposition, Svd, Machine L
Archive: download here


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