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From s..@apache.org
Subject svn commit: r1577992 - /mahout/site/mahout_cms/trunk/content/users/clustering/visualizing-sample-clusters.mdtext
Date Sun, 16 Mar 2014 08:08:53 GMT
Author: ssc
Date: Sun Mar 16 08:08:53 2014
New Revision: 1577992

URL: http://svn.apache.org/r1577992
Log:
cleaned up visualizing sample clusters

Modified:
    mahout/site/mahout_cms/trunk/content/users/clustering/visualizing-sample-clusters.mdtext

Modified: mahout/site/mahout_cms/trunk/content/users/clustering/visualizing-sample-clusters.mdtext
URL: http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/users/clustering/visualizing-sample-clusters.mdtext?rev=1577992&r1=1577991&r2=1577992&view=diff
==============================================================================
--- mahout/site/mahout_cms/trunk/content/users/clustering/visualizing-sample-clusters.mdtext
(original)
+++ mahout/site/mahout_cms/trunk/content/users/clustering/visualizing-sample-clusters.mdtext
Sun Mar 16 08:08:53 2014
@@ -1,76 +1,48 @@
 Title: Visualizing Sample Clusters
+
 <a name="VisualizingSampleClusters-Introduction"></a>
 # Introduction
 
 Mahout provides examples to visualize sample clusters that gets created by
-various clustering algorithms like
-* Canopy Clustering
-* Dirichlet Process
-* KMeans
-* Fuzzy KMeans
-* MeanShift Canopy
-* Spectral KMeans
-* MinHash
-
-<a name="VisualizingSampleClusters-Note"></a>
-##### Note
-These are Swing programs. You have to be in a window system on the same
-machine you run these, or logged in via a "remote desktop" or VNC program.
+our clustering algorithms. Note that the visualization is done by Swing programs. You have
to be in a window system on the same
+machine you run these, or logged in via a remote desktop.
 
 <a name="VisualizingSampleClusters-Pre-Prep"></a>
 # Pre - Prep
 
-For visualizing the clusters, you would just have to execute the Java
-classes under org.apache.mahout.clustering.display package in
-mahout-examples module. If you are using eclipse, setup mahout-examples as
-a project as specified in [Working with Maven in Eclipse](buildingmahout#mahout_maven_eclipse.html)
-.
+For visualizing the clusters, you have to execute the Java
+classes under *org.apache.mahout.clustering.display* package in
+mahout-examples module. The easiest way to achieve this is to [setup Mahout](users/basics/quickstart.html)
in your IDE.
 
 <a name="VisualizingSampleClusters-Visualizingclusters"></a>
 # Visualizing clusters
 
-The following classes in org.apache.mahout.clustering.display can be run
+The following classes in *org.apache.mahout.clustering.display* can be run
 without parameters to generate a sample data set and run the reference
 clustering implementations over them:
-1. DisplayClustering - generates 1000 samples from three, symmetric
+
+1. **DisplayClustering** - generates 1000 samples from three, symmetric
 distributions. This is the same data set that is used by the following
 clustering programs. It displays the points on a screen and superimposes
 the model parameters that were used to generate the points. You can edit
-the generateSamples() method to change the sample points used by these
+the *generateSamples()* method to change the sample points used by these
 programs.
-1. DisplayClustering - displays initial areas of generated points
-1. DisplayDirichlet - uses Dirichlet Process clustering
-1. DisplayCanopy - uses Canopy clustering
-1. DisplayKMeans - uses k-Means clustering
-1. DisplayFuzzyKMeans - uses Fuzzy k-Means clustering
-1. DisplayMeanShift - uses MeanShift clustering
-1. DisplaySpectralKMeans - uses Spectral KMeans via map-reduce algorithm
-
-If you are using Eclipse and have set it up as specified in Pre-Prep, just
-right-click on each of the classes mentioned above and choose "Run As -
-Java Application". To run these directly from the command line:
+1. **DisplayClustering** - displays initial areas of generated points
+1. **DisplayCanopy** - uses Canopy clustering
+1. **DisplayKMeans** - uses k-Means clustering
+1. **DisplayFuzzyKMeans** - uses Fuzzy k-Means clustering
+1. **DisplaySpectralKMeans** - uses Spectral KMeans via map-reduce algorithm
+
+If you are using Eclipse, just right-click on each of the classes mentioned above and choose
"Run As -Java Application". To run these directly from the command line:
 
     cd $MAHOUT_HOME/examples
-    mvn -q exec:java
--Dexec.mainClass=org.apache.mahout.clustering.display.DisplayClustering
-    # substitute other names above for DisplayClustering
-    # Note: the DisplaySpectralKMeans program does a Hadoop job that takes 3
-minutes on a laptop. Set this MVN_OPTS=300m to give the program enough
-memory. You may find that some of the other programs also need more memory.
+    mvn -q exec:java -Dexec.mainClass=org.apache.mahout.clustering.display.DisplayClustering
 
+You can substitute other names above for *DisplayClustering*. 
 
-Note:
-* Some of these programs display the sample points and then superimpose all
-of the clusters from each iteration. The last iteration's clusters are in
+
+Note that some of these programs display the sample points and then superimpose all of the
clusters from each iteration. The last iteration's clusters are in
 bold red and the previous several are colored (orange, yellow, green, blue,
 magenta) in order after which all earlier clusters are in light grey. This
 helps to visualize how the clusters converge upon a solution over multiple
-iterations.
-
-* By changing the parameter values (k, ALPHA_0, numIterations) and the
-display SIGNIFICANCE you can obtain different results.
-
-<a name="VisualizingSampleClusters-ScreenCaptureAnimation"></a>
-# Screen Capture Animation
-See [Sample Clusters Animation](sample-clusters-animation.html)
- for a screen caps of all the above programs, and an animated gif.
+iterations.
\ No newline at end of file



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