Apache Mahout 0.9 released

[Suneel Marthi <su...@yahoo.com>]

The Apache Mahout PMC is pleased to announce the release of Mahout 0.9.
Mahout's goal is to build scalable machine learning libraries focused
primarily in the areas of collaborative filtering (recommenders),
clustering and classification (known collectively as the "3Cs"), as well as the
necessary infrastructure to support those implementations including, but
not limited to, math packages for statistics, linear algebra and others
as well as Java primitive collections, local and distributed vector and
matrix classes and a variety of integrative code to work with popular
packages like Apache Hadoop, Apache Lucene, Apache HBase, Apache
Cassandra and much more. The 0.9 release is mainly a clean up release in
preparation for an upcoming 1.0 release targeted for first half of 2014, but there are a few
significant new features, which are highlighted below.

To get started with Apache Mahout 0.9, download the release artifacts and signatures at http://www.apache.org/dyn/closer.cgi/mahout or visit the central Maven repository.

As with any release, we wish to thank all of the users and contributors
to Mahout. Please see the CHANGELOG [1] and JIRA Release Notes [2] for
individual credits, as there are too many to list here.

GETTING STARTED

In the release package, the examples directory contains several working examples of the core
functionality available in Mahout. These can be run via scripts in the examples/bin
directory and will prompt you for more information to help you try things out. 
Most examples do not need a Hadoop cluster in order to run.

RELEASE HIGHLIGHTS

The highlights of the Apache Mahout 0.9 release include, but are not
limited to the list below. For further information, see the included CHANGELOG[1] file.

-  MAHOUT-1245: A new and improved Mahout website based on Apache CMS
-  MAHOUT-1265: MultiLayer Perceptron (MLP) classifier 
   This is an early implementation of MLP to solicit user feedback, needs to be integrated into Mahout’s processing pipeline to work with Mahout’s vectors.
-  MAHOUT-1297: Scala DSL Bindings for Mahout Math Linear Algebra.  See http://weatheringthrutechdays.blogspot.com/2013/07/scala-dsl-for-mahout-in-core-linear.html
-  MAHOUT-1288: Recommenders as a Search.  See https://github.com/pferrel/solr-recommender
-  MAHOUT-1300: Suport for easy functional Matrix views and derivatives
-  MAHOUT-1343: JSON output format for ClusterDumper
-  MAHOUT-1345: Enable randomised testing for all Mahout modules using Carrot RandomizedRunner. 
-  MAHOUT-1361: Online Algorithm for computing accurate Quantiles using 1-dimensional Clustering.  See https://github.com/tdunning/t-digest/blob/master/docs/theory/t-digest-paper/histo.pdf for the details.
-  MAHOUT-1364: Upgrade Mahout to Lucene 4.6.1


- Removed Deprecated algorithms as they have been either replaced by better performing algorithms or lacked user support and maintenance.

- the usual bug fixes. See [2] for more information on the 0.9 release.

A total of 113 separate JIRA issues were addressed in this release.

The following algorithms that were marked deprecated in 0.8 have been removed in 0.9:

- From Clustering:
   Switched LDA implementation from using Gibbs Sampling to Collapsed Variational Bayes (CVB)

  Meanshift

  MinHash - removed due to poor performance,  lack of support and lack of usage

- From Classification (both are sequential implementations)

  Winnow - lack of actual usage and support

  Perceptron - lack of actual usage and support

- Collaborative Filtering
    SlopeOne implementations in org.apache.mahout.cf.taste.hadoop.slopeone and org.apache.mahout.cf.taste.impl.recommender.slopeone
    Distributed pseudo recommender in org.apache.mahout.cf.taste.hadoop.pseudo
    TreeClusteringRecommender in org.apache.mahout.cf.taste.impl.recommender

- Mahout Math
    Hadoop entropy stuff in org.apache.mahout.math.stats.entropy


CONTRIBUTING

Mahout is always looking for contributions focused on the 3Cs. If you are
interested in contributing, please see our contribution page http://mahout.apache.org/developers/how-to-contribute.html or contact us via email at dev@mahout.apache.org.


As the project moves towards a 1.0 release, the community will be focused on key algorithms that are proven to scale in production and have seen wide-spread adoption. 

[1] http://svn.apache.org/viewvc/mahout/trunk/CHANGELOG?view=markup&pathrev=1563661
[2] https://issues.apache.org/jira/browse/MAHOUT-1411?jql=project%20%3D%20MAHOUT%20AND%20fixVersion%20%3D%20%220.9%22