What is Apache Mahout?
Posted by Armando Brito Mendes | Filed under software
Um exemplo dos muitos projetos open source for big data
The Apache Mahout™ machine learning library’s goal is to build scalable machine learning libraries.
Mahout currently has
- User and Item based recommenders
- Matrix factorization based recommenders
- K-Means, Fuzzy K-Means clustering
- Latent Dirichlet Allocation
- Singular value decomposition
- Logistic regression based classifier
- Complementary Naive Bayes classifier
- Random forest decision tree based classifier
- High performance java collections (previously colt collections)
- A vibrant community
With scalable we mean:
Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms
Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license.
Tags: big data, data mining, DW \ BI
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