Hi All, Please see below the first draft of Release notes for Mahout 0.9. Please feel free to add/edit sections as u see fit. (This is a draft only).
Regards, Suneel --------------------------------- 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. In addition to the release highlights and artifacts, please pay attention to the section labelled FUTURE PLANS below for more information about upcoming releases of Mahout. 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 file. - Scala DSL Bindings for Mahout Math Linear Algebra (MAHOUT-1297). See http://weatheringthrutechdays.blogspot.com/2013/07/scala-dsl-for-mahout-in-core-linear.html - New Multilayer Perceptron Classifier (MAHOUT-1265) - Recommenders as a Search (MAHOUT-1288). See https://github.com/pferrel/solr-recommender - MAHOUT-1364: Upgrade Mahout to be Lucene 4.6.0 compliant - 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. - Removed Deprecated algorithms. - the usual bug fixes. See JIRA [?} for more information on the 0.9 release. A total 91 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: Dirichlet - replaced by Collapsible Variational Bayes (CVB) Meanshift MinHash - removed due to poor performance and lack of usage EigenCuts - - From Classification (both are sequential implementations) Winnow - lack of actual usage Perceptron - lack of actual usage - Frequent Pattern Mining - Collaborative Filtering All recommenders in org.apache.mahout.cf.taste.impl.recommender.knn 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 Lanczos in favour of SSVD Hadoop entropy stuff in org.apache.mahout.math.stats.entropy If you are interested in supporting 1 or more of these algorithms, please make it known on [email protected] and via JIRA issues that fix and/or improve them. Please also provide supporting evidence as to their effectiveness for you in production. CONTRIBUTING Mahout is always looking for contributions focused on the 3Cs. If you are interested in contributing, please see our contribution page, https://cwiki.apache.org/MAHOUT/how-to-contribute.html, on the Mahout wiki or contact us via email at [email protected]. FUTURE PLANS 1.0 Plans ------------ - New Downpour SGD classifier - Support for Finite State Transducers (FST) as a Dictionary Type. - Support for Hadoop 2.x - Port Mahout's recommenders to Spark (??) - Support for Java 7 - Better API interfaces for Clustering - (what else???) 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. Our plans as a community are to focus 1.0 on the support of algorithms and features listed above. The support for the algorithms packaged in 1.0 for atleast two minor versions after 1.0 release. In the case of removal after 1.0, we will deprecate the functionality in the 1.(x+1) minor release and remove it in the 1.(x+2) release. For instance, if feature X is to be removed after the 1.2 release, it will be deprecated in 1.3 and removed in 1.4. [1] http://svn.apache.org/viewvc/mahout/trunk/CHANGELOG?revision=1552746&view=markup [2] https://issues.apache.org/jira/browse/MAHOUT-1376?jql=project%20%3D%20MAHOUT%20AND%20fixVersion%20%3D%20%220.9%22
