[Python-modules-commits] r5529 - in packages/python-cluster/trunk/debian (control)

[luciano at users.alioth.debian.org]

    Date: Friday, May 30, 2008 @ 01:07:55
  Author: luciano
Revision: 5529

removing double space in the description

Modified:
  packages/python-cluster/trunk/debian/control

Modified: packages/python-cluster/trunk/debian/control
===================================================================
--- packages/python-cluster/trunk/debian/control	2008-05-30 00:07:19 UTC (rev 5528)
+++ packages/python-cluster/trunk/debian/control	2008-05-30 01:07:55 UTC (rev 5529)
@@ -14,12 +14,12 @@
 Depends: ${python:Depends}, ${misc:Depends}
 XB-Python-Version: ${python:Versions}
 Description: allows grouping a list of arbitrary objects into related groups (clusters)
-  python-cluster is a "simple" package that allows to create several groups
-  (clusters) of objects from a list. It's meant to be flexible and able to
-  cluster any object. To ensure this kind of flexibility, you need not only to
-  supply the list of objects, but also a function that calculates the similarity
-  between two of those objects. For simple datatypes, like integers, this can be
-  as simple as a subtraction, but more complex calculations are possible. Right
-  now, it is possible to generate the clusters using a hierarchical clustering
-  and the popular K-Means algorithm. For the hierarchical algorithm there are
-  different "linkage" (single, complete, average and uclus) methods available.
+ python-cluster is a "simple" package that allows to create several groups
+ (clusters) of objects from a list. It's meant to be flexible and able to
+ cluster any object. To ensure this kind of flexibility, you need not only to
+ supply the list of objects, but also a function that calculates the similarity
+ between two of those objects. For simple datatypes, like integers, this can be
+ as simple as a subtraction, but more complex calculations are possible. Right
+ now, it is possible to generate the clusters using a hierarchical clustering
+ and the popular K-Means algorithm. For the hierarchical algorithm there are
+ different "linkage" (single, complete, average and uclus) methods available.