Monday, March 5, 2012
Apache Mahout is a new open source project by the Apache Software Foundation (ASF) with the primary goal of creating scalable machine-learning algorithms that are free to use under the Apache license. Mahout contains implementations for clustering, categorization, CF, and evolutionary programming. Furthermore, where prudent, it uses the Apache Hadoop library to enable Mahout to scale effectively in the cloud
A mahout is a person who keeps and drives an elephant. The name Mahout comes from the project's use of Apache Hadoop — which has a yellow elephant as its logo — for scalability and fault tolerance
Once the exclusive domain of academics and corporations with large research budgets, intelligent applications that learn from data and user input are becoming more common. The need for machine-learning techniques like clustering, collaborative filtering, and categorization has never been greater, be it for finding commonalities among large groups of people or automatically tagging large volumes of Web content. The Apache Mahout project aims to make building intelligent applications easier and faster. Mahout co-founder Grant Ingersoll introduces the basic concepts of machine learning and then demonstrates how to use Mahout to cluster documents, make recommendations, and organize content.
The Mahout project was started by several people involved in the Apache Lucene (open source search) community with an active interest in machine learning and a desire for robust, well-documented, scalable implementations of common machine-learning algorithms for clustering and categorization. The community was initially driven by Ng et al.'s paper "Map-Reduce for Machine Learning on Multicore" but has since evolved to cover much broader machine-learning approaches.