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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.
Algorithms are based on the document found at http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html/
The above site is no longer avaialble, but you can still view it in the internet archive at: https://web.archive.org/web/20070912040206/http://home.dei.polimi.it//matteucc/Clustering/tutorial_html/
A simple python program could look like this:
>>> from cluster import HierarchicalClustering >>> 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.
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