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From smar...@apache.org
Subject svn commit: r1481556 - in /mahout/trunk: CHANGELOG pom.xml
Date Sun, 12 May 2013 13:50:59 GMT
Author: smarthi
Date: Sun May 12 13:50:58 2013
New Revision: 1481556

URL: http://svn.apache.org/r1481556
Log:
Mahout-1207: Fix typos in description in parent pom

Modified:
    mahout/trunk/CHANGELOG
    mahout/trunk/pom.xml

Modified: mahout/trunk/CHANGELOG
URL: http://svn.apache.org/viewvc/mahout/trunk/CHANGELOG?rev=1481556&r1=1481555&r2=1481556&view=diff
==============================================================================
--- mahout/trunk/CHANGELOG (original)
+++ mahout/trunk/CHANGELOG Sun May 12 13:50:58 2013
@@ -2,6 +2,8 @@ Mahout Change Log
 
 Release 0.8 - unreleased
 
+  MAHOUT-1207: Fix typos in description in parent pom (Stevo Slavic via smarthi)  
+
   MAHOUT-1199: Improve javadoc comments of mahout-integration (Angel Martinez Gonzalez via
smarthi)  
 
   MAHOUT-1162: Adding BallKMeans and StreamingKMeans clustering algorithms (dfilimon)

Modified: mahout/trunk/pom.xml
URL: http://svn.apache.org/viewvc/mahout/trunk/pom.xml?rev=1481556&r1=1481555&r2=1481556&view=diff
==============================================================================
--- mahout/trunk/pom.xml (original)
+++ mahout/trunk/pom.xml Sun May 12 13:50:58 2013
@@ -33,7 +33,7 @@
   <url>http://mahout.apache.org</url>
   <inceptionYear>2008</inceptionYear>
   <description>Mahout's goal is to build scalable machine learning libraries. With
scalable we mean: Scalable to
-    reasonably large data sets. Our core algorithms for clustering, classfication and batch
based collaborative
+    reasonably large data sets. Our core algorithms for clustering, classification and batch
based collaborative
     filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However
we do not restrict
     contributions to Hadoop based implementations: Contributions that run on a single node
or on a non-Hadoop
     cluster are welcome as well. The core libraries are highly optimized to allow for good
performance also for
@@ -42,7 +42,7 @@
     diverse community to facilitate discussions not only on the project itself but also on
potential use cases. Come
     to the mailing lists to find out more. Currently Mahout supports mainly four use cases:
Recommendation mining
     takes users' behavior and from that tries to find items users might like. Clustering
takes e.g. text documents
-    and groups them into groups of topically related documents. Classification learns from
exisiting categorized
+    and groups them into groups of topically related documents. Classification learns from
existing categorized
     documents what documents of a specific category look like and is able to assign unlabelled
documents to the
     (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms
in a query session,
     shopping cart content) and identifies, which individual items usually appear together.



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