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.pdffor > 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. >
