Below r the release notes, not sure where they should be going on the website. If someone could point me to a location I will go ahead and update the same.
============================= 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 On Monday, February 17, 2014 3:27 PM, Ellen Friedman <b.ellen.fried...@gmail.com> wrote: Hi Suneel, Thanks for notes. I'm inquiring about status of the notes and update to the website to announce 0.9: Ted has reviewed the release notes - were you waiting for additional input or are they ready to go on the website? Are you the one who updates the site? I've been asked to write a short blog on the release but wanted to wait until the site is updated. Thanks much Ellen On Tue, Feb 11, 2014 at 10:06 AM, Suneel Marthi <suneel_mar...@yahoo.com> wrote: Here's a draft of the Release Notes for Mahout 0.9, Please review the same. > >---------------------------------- > > > >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-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-1364: Upgrade Mahout to Lucene 4.6.1 > >- 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-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. > >- 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 Dirtichlet 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 > > > > > > > > > >On Monday, December 23, 2013 7:41 PM, Dmitriy Lyubimov <dlie...@gmail.com> >wrote: > >On Sun, Dec 22, 2013 at 11:21 AM, Sebastian Schelter < > >ssc.o...@googlemail.com> wrote: > >> >> > >> > - Mahout Math >> > Lanczos in favour of SSVD >> >> IIRC, we agreed to not remove Lanczos, although it was initially >> deprecated. We should undeprecate it. >> >> >Some folks like Lanczos in Mahout (for reasons not really clear to me, >aside from accuracy when computing svd of a random noise, there are >actually 0 reasons to use Lanczos instead). I agree we don't necessarily >want to cull it out -- but IMO there should be a clear steer posted in >favor of SSVD in the docs/javadocs.
