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cluster 1.3.1


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


The above site is no longer avaialble, but you can still view it in the internet archive at:


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.

File Type Py Version Uploaded on Size
cluster-1.3.1-py2.py3-none-any.whl (md5) Python Wheel py2.py3 2015-10-16 19KB
cluster-1.3.1.tar.gz (md5) Source 2015-10-16 39KB
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