Author: tn
Date: Wed Mar 27 21:54:36 2013
New Revision: 1461866
URL: http://svn.apache.org/r1461866
Log:
Add clarification about default distance measure to javadoc.
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/clustering/DBSCANClusterer.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/clustering/KMeansPlusPlusClusterer.java
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/clustering/DBSCANClusterer.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/clustering/DBSCANClusterer.java?rev=1461866&r1=1461865&r2=1461866&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/clustering/DBSCANClusterer.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/clustering/DBSCANClusterer.java
Wed Mar 27 21:54:36 2013
@@ -74,6 +74,8 @@ public class DBSCANClusterer<T extends C
/**
* Creates a new instance of a DBSCANClusterer.
+ * <p>
+ * The euclidean distance will be used as default distance measure.
*
* @param eps maximum radius of the neighborhood to be considered
* @param minPts minimum number of points needed for a cluster
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/clustering/KMeansPlusPlusClusterer.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/clustering/KMeansPlusPlusClusterer.java?rev=1461866&r1=1461865&r2=1461866&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/clustering/KMeansPlusPlusClusterer.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/clustering/KMeansPlusPlusClusterer.java
Wed Mar 27 21:54:36 2013
@@ -75,6 +75,8 @@ public class KMeansPlusPlusClusterer<T e
* <p>
* The default strategy for handling empty clusters that may appear during
* algorithm iterations is to split the cluster with largest distance
variance.
+ * <p>
+ * The euclidean distance will be used as default distance measure.
*
* @param k the number of clusters to split the data into
*/
@@ -86,6 +88,8 @@ public class KMeansPlusPlusClusterer<T e
* <p>
* The default strategy for handling empty clusters that may appear during
* algorithm iterations is to split the cluster with largest distance
variance.
+ * <p>
+ * The euclidean distance will be used as default distance measure.
*
* @param k the number of clusters to split the data into
* @param maxIterations the maximum number of iterations to run the
algorithm for.
