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From "Suneel Marthi (Confluence)" <conflue...@apache.org>
Subject [CONF] Apache Mahout > Release 0.8
Date Thu, 25 Jul 2013 05:08:01 GMT
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    <h2><a href="https://cwiki.apache.org/confluence/display/MAHOUT/Release+0.8">Release
0.8</a></h2>
    <h4>Page <b>edited</b> by             <a href="https://cwiki.apache.org/confluence/display/~smarthi">Suneel
Marthi</a>
    </h4>
        <br/>
                         <h4>Changes (1)</h4>
                                 
    
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                    <table class="diff" cellpadding="0" cellspacing="0">
    
            <tr><td class="diff-snipped" >...<br></td></tr>
            <tr><td class="diff-unchanged" > <br>- Numerous performance
improvements to Vector and Matrix implementations, API&#39;s and their iterators <br></td></tr>
            <tr><td class="diff-changed-lines" >- MAHOUT-944:  Support for converting
one or more Lucene storage indexes to SequenceFiles as well as an upgrade of the supported
Lucene <span class="diff-changed-words">version<span class="diff-added-chars"style="background-color:
#dfd;"> to Lucene 4</span>.<span class="diff-added-chars"style="background-color:
#dfd;">3.1.</span></span> <br></td></tr>
            <tr><td class="diff-unchanged" >- MAHOUT-1154 and friends: New streaming
k-means implementation that offers on-line (and fast) clustering <br>- MAHOUT-833: Make
conversion to SequenceFiles Map-Reduce, &#39;seqdirectory&#39; can now be run as a
MapReduce job. <br></td></tr>
            <tr><td class="diff-snipped" >...<br></td></tr>
    
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    </div>                            <h4>Full Content</h4>
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        <p>DRAFT RELEASE NOTES FOR 0.8</p>
<blockquote>
<p>The Apache Mahout PMC is pleased to announce the release of Mahout 0.8.  Mahout's
goal is to build scalable machine learning libraries focused primarily in the areas of collaborative
filtering (recommenders), clustering and classification (known 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 0.8 release is mainly a clean up release in preparation for an upcoming 1.0
release, but there are several significant new features, which are highlighted below.</p>

<p>To get started with Apache Mahout 0.8, download the release artifacts and signatures
at <a href="http://www.apache.org/dyn/closer.cgi/mahout" class="external-link" rel="nofollow">http://www.apache.org/dyn/closer.cgi/mahout</a>.
 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.  Most examples do
not need a Hadoop cluster in order to run.</p>

<p>Please pay attention to the section labelled FUTURE PLANS below for more information
about upcoming releases of Mahout.</p>

<p>As with any release, we wish to thank all of the users and contributors to Mahout.
 Please see the CHANGELOG and JIRA for individual credits, as there are too many to list here.</p>

<p>RELEASE HIGHLIGHTS</p>

<p>The highlights of the Apache Mahout 0.8 release include, but are not limited to the
list below.  For further information, see the included CHANGELOG file.</p>

<ul class="alternate" type="square">
	<li>Numerous performance improvements to Vector and Matrix implementations, API's and
their iterators</li>
	<li>MAHOUT-944:  Support for converting one or more Lucene storage indexes to SequenceFiles
as well as an upgrade of the supported Lucene version to Lucene 4.3.1.</li>
	<li>MAHOUT-1154 and friends: New streaming k-means implementation that offers on-line
(and fast) clustering</li>
	<li>MAHOUT-833: Make conversion to SequenceFiles Map-Reduce, 'seqdirectory' can now
be run as a MapReduce job.</li>
	<li>Mahout-884: Matrix Concat utility, presently only concatenates two matrices.</li>
	<li>The usual bug fixes.  See JIRA for more information on the 0.8 release.</li>
</ul>


<p>A total of 174 separate JIRA issues are addressed in this release.</p>

<p>CONTRIBUTING</p>

<p>Mahout is always looking for contributions focused on the 3Cs.  If you are interested
in contributing, please see our <a href="https://cwiki.apache.org/MAHOUT/how-to-contribute.html"
class="external-link" rel="nofollow">https://cwiki.apache.org/MAHOUT/how-to-contribute.html</a>
on the Mahout wiki or contact us via email at dev@mahout.apache.org.</p>

<p>FUTURE PLANS</p>

<p>0.9</p>

<p>As the project moves towards a 1.0 release, the community is working to clean up
and/or remove parts of the code base that are under-supported or that underperform as well
as to better focus the energy and contributions on key algorithms that are proven to scale
in production and have seen wide-spread adoption.  To this end, in the next release, the project
is planning on removing support for the following algorithms unless there is sustained support
and improvement of them before the next release.</p>

<p>The algorithms to be removed are:</p>
<ul class="alternate" type="square">
	<li>From Clustering:<br/>
	Dirichlet<br/>
	MeanShift<br/>
	MinHash</li>
	<li>From Classification (both are sequential implementations)<br/>
	Winnow<br/>
	Perceptron</li>
	<li>Frequent Pattern Mining</li>
	<li>Collaborative Filtering<br/>
	GSI: DO ANY GO HERE?</li>
</ul>


<p>If you are interested in supporting 1 or more of these algorithms, please make it
known on dev@mahout.apache.org and via JIRA issues that fix and/or improve them.  Please also
provide supporting evidence as to their effectiveness for you in production.</p>

<p>1.0 PLANS</p>

<p>Our plans as a community are to focus 0.9 on cleanup of bugs and the removal of the
code mentioned above and then to follow with a 1.0 release soon thereafter, at which point
the community is committing to the support of the algorithms packaged in the 1.0 for at least
two minor versions after their release.  In the case of removal, 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.</p></blockquote>
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