Simple clustering library for python.
|CHANGELOG||* non-svn metadata updated ;)|
|INSTALL||Initial SVN import|
|LICENSE||Initial SVN import|
|MANIFEST.in||Initial SVN import|
|README||Initial SVN import|
|cluster.bmp||Initial SVN import|
|cluster.py||Fix for issue #10|
|makedist.sh||Initial SVN import|
|setup.cfg||Initial SVN import|
|setup.py||Version bump to 1.1.2|
|test.py||Fix for issue #10|
DESCRIPTION =========== 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. I plan to implement other algoithms as well on an "as-needed" or "as-I-have-time" basis. Algorithms are based on the document found at http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html/ USAGE ===== A simple python program could look like this:: >>> from cluster import * >>> data = [12,34,23,32,46,96,13] >>> cl = HierarchicalClustering(data, lambda x,y: abs(x-y)) >>> cl.getlevel(10) # get clusters of items closer than 10 [96, 46, [12, 13, 23, 34, 32]] >>> cl.getlevel(5) # get clusters of items closer than 5 [96, 46, [12, 13], 23, [34, 32]] Note, that when you retrieve a set of clusters, it immediately starts the clustering process, which is quite complex. If you intend to create clusters from a large dataset, consider doing that in a separate thread. For K-Means clustering it would look like this: >>> from cluster import KMeansClustering >>> cl = KMeansClustering([(1,1), (2,1), (5,3), ...]) >>> clusters = cl.getclusters(2) The parameter passed to getclusters is the count of clusters generated.