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Posted to commits@mahout.apache.org by is...@apache.org on 2013/11/21 12:17:28 UTC
svn commit: r1544119 -
/mahout/site/mahout_cms/trunk/content/users/clustering/mean-shift-clustering.mdtext
Author: isabel
Date: Thu Nov 21 11:17:27 2013
New Revision: 1544119
URL: http://svn.apache.org/r1544119
Log:
MAHOUT-1245 - formatting fixes
Modified:
mahout/site/mahout_cms/trunk/content/users/clustering/mean-shift-clustering.mdtext
Modified: mahout/site/mahout_cms/trunk/content/users/clustering/mean-shift-clustering.mdtext
URL: http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/users/clustering/mean-shift-clustering.mdtext?rev=1544119&r1=1544118&r2=1544119&view=diff
==============================================================================
--- mahout/site/mahout_cms/trunk/content/users/clustering/mean-shift-clustering.mdtext (original)
+++ mahout/site/mahout_cms/trunk/content/users/clustering/mean-shift-clustering.mdtext Thu Nov 21 11:17:27 2013
@@ -1,11 +1,12 @@
Title: Mean Shift Clustering
-"Mean Shift: A Robust Approach to Feature Space Analysis"
-(http://www.caip.rutgers.edu/riul/research/papers/pdf/mnshft.pdf)
+
+# Means Shift clustering
+
+["Mean Shift: A Robust Approach to Feature Space Analysis"](http://www.caip.rutgers.edu/riul/research/papers/pdf/mnshft.pdf)
introduces the geneology of the mean shift custering procedure which dates
back to work in pattern recognition in 1975. The paper contains a detailed
derivation and several examples of the use of mean shift for image smooting
-and segmentation. "Mean Shift Clustering"
-(http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/TUZEL1/MeanShift.pdf)
+and segmentation. ["Mean Shift Clustering"](http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/TUZEL1/MeanShift.pdf)
presents an overview of the algorithm with a summary of the derivation. An
attractive feature of mean shift clustering is that it does not require
a-priori knowledge of the number of clusters (as required in k-means) and
@@ -70,8 +71,7 @@ Invocation using the command line takes
bin/mahout meanshift \
-i <input vectors directory> \
-o <output working directory> \
- -inputIsCanopies <input directory contains mean shift canopies not
-vectors> \
+ -inputIsCanopies <input directory contains mean shift canopies not vectors> \
-dm <DistanceMeasure> \
-t1 <the T1 threshold> \
-t2 <the T2 threshold> \
@@ -141,7 +141,7 @@ The points are generated as follows:
In the first image, the points are plotted and the 3-sigma boundaries of
their generator are superimposed.
-!SampleData.png!
+
In the second image, the resulting clusters (k=3) are shown superimposed
upon the sample data. In this image, each cluster renders in a different
@@ -150,11 +150,11 @@ centers determined by the algorithm. Mea
clustering this data, though by its design the cluster membership is unique
and the clusters do not overlap.
-!MeanShift.png!
+
The third image shows the results of running Mean Shift on a different data
set (see [Dirichlet Process Clustering](dirichlet-process-clustering.html)
for details) which is generated using asymmetrical standard deviations.
Mean Shift does an excellent job of clustering this data set too.
-!2dMeanShift.png!
+
\ No newline at end of file