Metadata-Version: 1.0
Name: cluster
Version: 1.1.1b3
Summary: python-cluster is a "simple" package that allows to create several groups
(clusters) of objects from a list
Home-page: http://python-cluster.sourceforge.net/
Author: Michel Albert
Author-email: exhuma@users.sourceforge.net
License: LGPL
Description: 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.
        
Platform: UNKNOWN
