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https://issues.apache.org/jira/browse/MAHOUT-1162?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13654365#comment-13654365
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Hudson commented on MAHOUT-1162:
--------------------------------
Integrated in Mahout-Quality #1988 (See
[https://builds.apache.org/job/Mahout-Quality/1988/])
MAHOUT-1162: Adding BallKMeans and StreamingKMeans clustering algorithms
Additionally, besides the new algorithms there are new utility methods in
ClusteringUtils for casting between Vector types and computing various
clustering quality metrics: Dunn Index, Davies-Bouldin Index and the
Adjusted Rand Index. (Revision 1480954)
Result = SUCCESS
dfilimon :
http://svn.apache.org/viewcvs.cgi/?root=Apache-SVN&view=rev&rev=1480954
Files :
* /mahout/trunk/CHANGELOG
*
/mahout/trunk/core/src/main/java/org/apache/mahout/clustering/ClusteringUtils.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/clustering/streaming
*
/mahout/trunk/core/src/main/java/org/apache/mahout/clustering/streaming/cluster
*
/mahout/trunk/core/src/main/java/org/apache/mahout/clustering/streaming/cluster/BallKMeans.java
*
/mahout/trunk/core/src/main/java/org/apache/mahout/clustering/streaming/cluster/StreamingKMeans.java
* /mahout/trunk/core/src/test/java/org/apache/mahout/clustering/streaming
*
/mahout/trunk/core/src/test/java/org/apache/mahout/clustering/streaming/cluster
*
/mahout/trunk/core/src/test/java/org/apache/mahout/clustering/streaming/cluster/BallKMeansTest.java
*
/mahout/trunk/core/src/test/java/org/apache/mahout/clustering/streaming/cluster/DataUtils.java
*
/mahout/trunk/core/src/test/java/org/apache/mahout/clustering/streaming/cluster/StreamingKMeansTest.java
> Adding BallKMeans and StreamingKMeans classes
> ---------------------------------------------
>
> Key: MAHOUT-1162
> URL: https://issues.apache.org/jira/browse/MAHOUT-1162
> Project: Mahout
> Issue Type: New Feature
> Components: Clustering
> Affects Versions: 0.8
> Reporter: Dan Filimon
> Attachments: MAHOUT_1162_with_test.patch
>
>
> Adding BallKMeans and StreamingKMeans clustering algorithms.
> These both implement Iterable<Centroid> and thus return the resulting
> centroids after clustering.
> BallKMeans implements:
> - kmeans++ initialization;
> - a normal k-means pass;
> - a trimming threshold so that points that are too far from the cluster they
> were assigned to are not used in the new centroid computation.
> StreamingKMeans implements
> [http://books.nips.cc/papers/files/nips24/NIPS2011_1271.pdf]:
> - an online clustering algorithm that takes each point into account one by one
> - for each point, it computes the distance to the nearest existing cluster
> - if the distance is greater than a set distanceCutoff, it will create a
> new cluster, otherwise it might be added to the cluster it's closest to
> (proportional to the value of the distance / distanceCutoff)
> - if there are too many clusters, the clusters will be *collapsed* (the
> same method gets called, but the number of clusters is re-adjusted)
> - finally, *about as many* clusters as requested are returned (not precise!);
> this represents a sketch of the original points.
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