[ANNOUNCE] Apache Mahout 0.12.1 Release

[Suneel Marthi <sm...@apache.org>]

The Apache Mahout PMC is pleased to announce the release of Mahout 0.12.1
which is a minor release following 0.12.0 release on April 11, 2016.
Mahout's goal is to create an environment for quickly creating machine
learning applications that scale and run on the highest performance
parallel computation engines available. Mahout comprises an interactive
environment and library that supports generalized scalable linear algebra
and includes many modern machine learning algorithms.

Mahout 0.12.1 is a maintenance release over Mahout 0.12.0 addresses the
following issues with Apache Flink integration:

MAHOUT-1859:  Disable non working msurf and mgrid before Mahout 0.12.1
release

MAHOUT-1848:  drmSampleKRows in FlinkEngine should generate a dense or
sparse matrix

MAHOUT-1847: drmSampleRows in FlinkEngine doesn't wrap Int Keys when
ClassTag is of type Int

MAHOUT-1841: Matrices.symmetricUniformView(...) returning values in the
wrong range.

MAHOUT-1836:Order and add missing paramters for
DictionaryVectorizer.createTermFrequencyVectors() javadoc parameter
comments.

MAHOUT-1835 Remove countsPerPartition in Flink/blas/package.scala

MAHOUT-1834: Setup Travis CI for Mahout

MAHOUT-1833: Enhance svec function to accept cardinality as parameter

MAHOUT-1832: Upgrade Jackson version and references to 2.x

MAHOUT-1827: Suggested changes to homepage, how to contribute

Upgrade to Apache Flink 1.0.3

Experimental Mahout 2d and 3d plotting

Many thanks to all Apache committers and contributors.  Special thanks to Shane
Curcuru
<https://issues.apache.org/jira/secure/ViewProfile.jspa?name=curcuru>,
Edmond Luo and <mutekinoootoko at gmail dot com> for their contributions.

Future Roadmap:


   1.

   Zeppelin integration for Mahout on Spark.
   2.

   Plotting Capabilities for Mahout matrices and DRMs
   3.

   Many Online and Batch Algorithm additions.
   4.

   Support for Native Optimizations.
   5.

   Performance enhancements for Samsara Framework.
   6.

   Performance enhancements for Algebraic Operations.