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From build...@apache.org
Subject svn commit: r900052 - in /websites/staging/mahout/trunk/content: ./ users/basics/algorithms.html
Date Wed, 05 Mar 2014 00:25:39 GMT
Author: buildbot
Date: Wed Mar  5 00:25:39 2014
New Revision: 900052

Log:
Staging update by buildbot for mahout

Modified:
    websites/staging/mahout/trunk/content/   (props changed)
    websites/staging/mahout/trunk/content/users/basics/algorithms.html

Propchange: websites/staging/mahout/trunk/content/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Wed Mar  5 00:25:39 2014
@@ -1 +1 @@
-1574265
+1574268

Modified: websites/staging/mahout/trunk/content/users/basics/algorithms.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/basics/algorithms.html (original)
+++ websites/staging/mahout/trunk/content/users/basics/algorithms.html Wed Mar  5 00:25:39
2014
@@ -283,101 +283,51 @@
 
   <div id="content-wrap" class="clearfix">
    <div id="main">
-    <p><a name="Algorithms-Classification"></a></p>
-<h2 id="classification">Classification</h2>
-<p>A general introduction to the most common text classification algorithms
-can be found at Google Answers: <a href="http://answers.google.com/answers/main?cmd=threadview&amp;id=225316">http://answers.google.com/answers/main?cmd=threadview&amp;id=225316</a>
- For information on the algorithms implemented in Mahout (or scheduled for
-implementation) please visit the following pages.</p>
-<p>Fully supported:</p>
-<ul>
-<li><a href="../classification/logistic-regression.html">Logistic Regression</a>
(SGD) - model parameter selection can be done in Hadoop</li>
-<li><a href="../classification/bayesian.html">Naive Bayes/ Complementary Naive
Bayes</a> - training runs on Hadoop</li>
-<li><a href="../classification/random-forests.html">Random Forests</a>
- (<a href="http://issues.apache.org/jira/browse/MAHOUT-122">MAHOUT-122</a>, -
training is done in Hadoop
- <a href="http://issues.apache.org/jira/browse/MAHOUT-140">MAHOUT-140</a>, <a
href="http://issues.apache.org/jira/browse/MAHOUT-145">MAHOUT-145</a>)</li>
-<li><a href="../stuff/hidden-markov-models.html">Hidden Markov Models</a>
(see MAHOUT-627, MAHOUT-396, MAHOUT-734) - training is done in
-Map-Reduce</li>
-</ul>
-<p>Drafts only:</p>
+    <p><a name="Algorithms-CollaborativeFiltering"></a></p>
+<h1 id="collaborative-filtering">Collaborative Filtering</h1>
 <ul>
-<li>Online Passive Aggressive (see <a href="http://issues.apache.org/jira/browse/MAHOUT-702">MAHOUT-702</a>)</li>
+<li>User-Based Collaborative Filtering - <strong>single machine</strong></li>
+<li>Item-Based Collaborative Filtering - <strong>single machine / MapReduce</strong></li>
+<li>Matrix Factorization with Alternating Least Squares - <strong>single machine
/ MapReduce</strong></li>
+<li>Matrix Factorization with Alternating Least Squares  on Implicit Feedback- <strong>single
machine / MapReduce</strong></li>
+<li>Weighted Matrix Factorization, SVD++, Parallel SGD - <strong>single machine</strong></li>
+</ul>
+<p><a name="Algorithms-Classification"></a></p>
+<h1 id="classification">Classification</h1>
+<ul>
+<li><a href="../classification/logistic-regression.html">Logistic Regression</a>
- trained via SGD - <strong>single machine</strong></li>
+<li><a href="../classification/bayesian.html">Naive Bayes/ Complementary Naive
Bayes</a> - <strong>MapReduce</strong></li>
+<li>Random Forest - <strong>MapReduce</strong></li>
+<li><a href="../stuff/hidden-markov-models.html">Hidden Markov Models</a>
- <strong>single machine</strong></li>
 </ul>
 <p><a name="Algorithms-Clustering"></a></p>
-<h2 id="clustering">Clustering</h2>
-<p>For a more detailed explanation see <a href="http://en.wikipedia.org/wiki/Cluster_analysis">Wikipedia
page</a> or checkout our <a href="reference-reading.html">Reference Reading</a></p>
-<p>Fully supported:</p>
-<ul>
-<li><a href="../clustering/canopy-clustering.html">MAHOUT:Canopy Clustering</a>
- (<a href="https://issues.apache.org/jira/browse/MAHOUT-3">MAHOUT-3</a> - runs
on Hadoop</li>
-<li><a href="../clustering/k-means-clustering.html">K-Means Clustering</a>
- (<a href="https://issues.apache.org/jira/browse/MAHOUT-5">MAHOUT-5</a> - runs
on Hadoop</li>
-<li><a href="../clustering/fuzzy-k-means.html">Fuzzy K-Means</a>
- (<a href="https://issues.apache.org/jira/browse/MAHOUT-74">MAHOUT-74</a> - runs
on Hadoop</li>
-<li>[Expectation Maximization](../clustering/expectation-maximization.html (<a href="http://issues.apache.org/jira/browse/MAHOUT-28">MAHOUT-28</a>
- runs on Hadoop</li>
-<li><a href="../clustering/mean-shift-clustering.html">Mean Shift Clustering</a>
- (<a href="https://issues.apache.org/jira/browse/MAHOUT-15">MAHOUT-15</a> - runs
on Hadoop</li>
-<li><a href="../clustering/dirichlet-process-clustering.html">Dirichlet Process
Clustering</a>
- (<a href="http://issues.apache.org/jira/browse/MAHOUT-30">MAHOUT-30</a> - runs
on Hadoop</li>
-<li><a href="../clustering/latent-dirichlet-allocation.html">Latent Dirichlet
Allocation</a>
- (<a href="http://issues.apache.org/jira/browse/MAHOUT-123">MAHOUT-123</a>) -
runs on Hadoop</li>
-<li>Minhash Clustering (<a href="https://issues.apache.org/jira/browse/MAHOUT-344">MAHOUT-344</a>)
- runs on Hadoop</li>
-<li>kMeans++ streaming clustering - documentation missing</li>
-</ul>
-<p>Drafts only:</p>
-<ul>
-<li><a href="../clustering/hierarchical-clustering.html">Hierarchical Clustering</a>
- (<a href="http://issues.apache.org/jira/browse/MAHOUT-19">MAHOUT-19</a>, <a
href="https://issues.apache.org/jira/browse/MAHOUT-843">MAHOUT-843</a>)</li>
-<li><a href="../clustering/spectral-clustering.html">Spectral Clustering</a>
- (<a href="https://issues.apache.org/jira/browse/MAHOUT-363">MAHOUT-363</a>)</li>
-</ul>
-<p><a name="Algorithms-Dimensionreduction"></a></p>
-<h2 id="dimension-reduction">Dimension reduction</h2>
-<p>Fully supported:</p>
-<ul>
-<li><a href="dimensional-reduction.html">Singular Value Decomposition and other
Dimension Reduction Techniques</a>
- (available since 0.3)</li>
-<li>Stochastic Singular Value Decomposition with PCA workflow</li>
-</ul>
-<p>Drafts only:</p>
-<ul>
-<li><a href="principal-components-analysis.html">Principal Components Analysis</a>
- (PCA) </li>
-<li><a href="gaussian-discriminative-analysis.html">Gaussian Discriminative Analysis</a>
- (GDA) </li>
-</ul>
-<p><a name="Algorithms-EvolutionaryAlgorithms"></a></p>
-<h2 id="evolutionary-algorithms">Evolutionary Algorithms</h2>
-<p>NOTE:  Watchmaker support has been removed as of 0.7</p>
-<p>see also: <a href="http://issues.apache.org/jira/browse/MAHOUT-56">MAHOUT-56</a></p>
-<p>You will find here information, examples, use cases, etc. related to
-Evolutionary Algorithms.</p>
-<p>Introductions and Tutorials:</p>
-<ul>
-<li><a href="http://www.geatbx.com/docu/algindex.html">Evolutionary Algorithms
Introduction</a></li>
-<li><a href="mahout.ga.tutorial.html">How to distribute the fitness evaluation
using Mahout.GA</a></li>
-</ul>
-<p>Example: <a href="../stuff/traveling-salesman.html">Traveling Salesman</a></p>
-<p><a name="Algorithms-Recommenders/CollaborativeFiltering"></a></p>
-<h2 id="recommenders-collaborative-filtering">Recommenders / Collaborative Filtering</h2>
-<p>Mahout contains both simple non-distributed recommender implementations and
-distributed Hadoop-based recommenders.</p>
-<ul>
-<li><a href="../recommender/recommender-first-timer-faq.html">First-timer FAQ</a></li>
-<li><a href="../recommender/recommender-documentation.html">Non-distributed recommenders
("Taste")</a></li>
-<li><a href="../recommender/itembased-collaborative-filtering.html">Distributed
Item-Based Collaborative Filtering</a></li>
-<li>Collaborative Filtering using a parallel matrix factorization
-<a name="Algorithms-Other"></a></li>
-</ul>
-<h2 id="other">Other</h2>
-<p>Fullly supported:</p>
-<ul>
-<li>RowSimilarityJob -- Builds an inverted index and then computes distances
-between items that have co-occurrences.  This is a fully distributed
-calculation.</li>
-<li>VectorDistanceJob -- Does a map side join between a set of "seed" vectors
-and all of the input vectors.</li>
-<li><a href="collocations.html">Collocations</a> ... find co-locations
of tokens in text, runs on Hadoop</li>
+<h1 id="clustering">Clustering</h1>
+<ul>
+<li><a href="../clustering/canopy-clustering.html">Canopy Clustering</a>
- <strong>MapReduce</strong></li>
+<li><a href="../clustering/k-means-clustering.html">k-Means Clustering</a>
- <strong>MapReduce</strong></li>
+<li><a href="../clustering/fuzzy-k-means.html">Fuzzy k-Means</a> - <strong>MapReduce</strong></li>
+<li>Streaming k-Means - <strong>single machine / MapReduce</strong> </li>
+<li><a href="../clustering/spectral-clustering.html">Spectral Clustering</a>
- <strong>MapReduce</strong> </li>
+</ul>
+<p><a name="Algorithms-DimensionalityReduction"></a></p>
+<h1 id="dimensionality-reduction">Dimensionality Reduction</h1>
+<ul>
+<li>Singular Value Decomposition - <strong>single machine</strong></li>
+<li>Lanczos Algorithm - <strong>single machine / MapReduce</strong></li>
+<li>Stochastic SVD - <strong>single machine / MapReduce</strong></li>
+<li>Principal Component Analysis (via Stochastic SVD)- <strong>single machine
/ MapReduce</strong></li>
+</ul>
+<p><a name="Algorithms-TopicModels"></a></p>
+<h1 id="topic-models">Topic Models</h1>
+<ul>
+<li><a href="../clustering/latent-dirichlet-allocation.html">Latent Dirichlet
Allocation</a> - <strong>single machine / MapReduce</strong></li>
+</ul>
+<p><a name="Algorithms-Miscellaneous"></a></p>
+<h2 id="miscellaneous">Miscellaneous</h2>
+<ul>
+<li>Frequent Pattern Mining - <strong>MapReduce</strong></li>
+<li>RowSimilarityJob - compute pairwise similarities between the rows of a matrix -
<strong>MapReduce</strong></li>
+<li><a href="collocations.html">Collocations</a> - find co-locations of
tokens in text - <strong>MapReduce</strong></li>
 </ul>
    </div>
   </div>     



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