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.
