[Scipy-svn] r4189 - trunk/scipy/cluster
scipy-svn@scip...
scipy-svn@scip...
Sun Apr 27 07:53:46 CDT 2008
Author: damian.eads
Date: 2008-04-27 07:53:43 -0500 (Sun, 27 Apr 2008)
New Revision: 4189
Modified:
trunk/scipy/cluster/vq.py
Log:
Tightening up the language of vq's module summary.
Modified: trunk/scipy/cluster/vq.py
===================================================================
--- trunk/scipy/cluster/vq.py 2008-04-27 12:52:45 UTC (rev 4188)
+++ trunk/scipy/cluster/vq.py 2008-04-27 12:53:43 UTC (rev 4189)
@@ -12,13 +12,13 @@
A vector v belongs to cluster i if it is closer to centroid i than
the other centroids. If v belongs to i, we say centroid i is the
- dominating centroid of v. Most variants of k-means try to minimize
- distortion, which is defined as the sum of the distances between
- each observation vector and its dominating centroid. Each step of
- the k-means algorithm refines the choices of centroids to reduce
- distortion. The change in distortion is often used as a stopping
- criterion: when the change is lower than a threshold, the k-means
- algorithm is not making progress and terminates.
+ dominating centroid of v. Common variants of k-means try to
+ minimize distortion, which is defined as the sum of the distances
+ between each observation vector and its dominating centroid. Each
+ step of the k-means algorithm refines the choices of centroids to
+ reduce distortion. The change in distortion is often used as a
+ stopping criterion: when the change is lower than a threshold, the
+ k-means algorithm is not making progress and terminates.
Since vector quantization is a natural application for k-means,
information theory terminology is often used. The centroid index
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