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Apache Mahout - 2 Demonstrations

12.22.2011
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Lucid Imagination recently hosted the first San Francisco Apache Mahout User Meeting on November 29th 2011. The 3 hours session was packed with talks by Grant Ingersoll from Lucid Imagination and Ted Dunning of MapR Technologies followed by networking, food and drinks. Session slides and videos are now available.

The first talk was given by Grant Ingersoll and he focused on using Mahout to cluster, classify and recommend email. Followed by a demonstration of using scripts packaged with Mahout. Here are the slides for this session.

Mahout 1 from Lucene Revolution on Vimeo.

 

The second talk was given by Ted Dunning who talked about how using random projections in machine learning can benefit performance without sacrificing quality. Session slides available here.

Mahout 2 from Lucene Revolution on Vimeo.



Source:  http://www.lucidimagination.com/blog/2011/12/13/apache-mahout-user-meeting-session-slides-and-videos-are-now-available/

 

Comments

Ash Mughal replied on Wed, 2012/01/25 - 7:18pm

The Apache Mahout machine learning library's goal is to build scalable machine learning libraries.

Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

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