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1.2.1
- Fixed an issue in multiprocessing code.
1.2.0
- Multiprocessing (by loisaidasam)
- Python 3 support
- Split up one big file into smaller more logical sub-modules
- Fixed https://github.com/exhuma/python-cluster/issues/11
- Documentation update.
1.1.1b3
- Fixed bug #1727558
- Some more unit-tests
- ValueError changed to ClusteringError where appropriate
1.1.1b2
- Fixed bug #1604859 (thanks to Willi Richert for reporting it)
1.1.1b1
- Applied patch [1535137] (thanks ajaksu)
--> Topology output supported
--> data and raw_data are now properties.
1.1.0b1
- KMeans Clustering implemented for simple numeric tuples.
Data in the form [(1,1), (2,1), (5,3), ...]
can be clustered.
Usage:
>>> from cluster import KMeansClustering
>>> cl = KMeansClustering([(1,1), (2,1), (5,3), ...])
>>> clusters = cl.getclusters(2)
the method "getclusters" takes the amount of clusters you would like to
have as parameter.
Only numeric values are supported in the tuples. The reason for this is
that the "centroid" method which I use, essentially returns a tuple of
floats. So you will lose any other kind of metadata. Once I figure out a
way how to recode that method, other types should be possible.
1.0.1b2
- Optimized calculation of the hierarchical clustering by using the fact, that
the generated matrix is symmetrical.
1.0.1b1
- Implemented complete-, average-, and uclus-linkage methods. You can select
one by specifying it in the constructor, for example:
cl = HierarchicalClustering(data, distfunc, linkage='uclus')
or by setting it before starting the clustering process:
cl = HierarchicalClustering(data, distfunc)
cl.setLinkageMethod('uclus')
cl.cluster()
- Clustering is not executed on object creation, but on the first call of
"getlevel". You can force the creation of the clusters by calling the
"cluster" method as shown above.