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From "Thomas Neidhart (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MATH-1031) Refactoring: Move variance calculation of a centroid cluster to its class
Date Sun, 01 Sep 2013 20:44:51 GMT

    [ https://issues.apache.org/jira/browse/MATH-1031?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13755804#comment-13755804
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Thomas Neidhart commented on MATH-1031:
---------------------------------------

I thought about this myself, but I was thinking about a different solution.
The MultiKMeans algorithm for example, uses the variance method to evaluate how "good" a clustering
has been, but this should be made more flexible, thus I would propose to create a new Interface,
e.g. "ClusterEvaluation", which performs this kind of calculation, and can be used at different
places. This interface can then be provided as an argument to the clustering algorithm and
later be used to evaluate the results.
                
> Refactoring: Move variance calculation of a centroid cluster to its class
> -------------------------------------------------------------------------
>
>                 Key: MATH-1031
>                 URL: https://issues.apache.org/jira/browse/MATH-1031
>             Project: Commons Math
>          Issue Type: Improvement
>    Affects Versions: 3.2
>            Reporter: Thorsten Schäfer
>            Priority: Minor
>         Attachments: centroid.patch
>
>
> Users might be interested in assessing the quality of each cluster in the calculated
clustering. This can be performed by calculating its variance. 
> The variance calculation is actually performed in other places (e.g. for the MultiKMeans),
but not available to end users. 
> I'd propose to add the functionality into the CentroidCluster. The one issue to consider
is that the cluster does not know based on which distance measure it was calculated. In the
implementation, I chose to parametrize the method with a distance measure which enables users
to also compare the quality based on various distance measures. Alternatively, it would be
possible to add the distance measure as a field, which is set by the clustering algorithm.
> In the patch I went for the first method and also changed the 2 other places where variance
calculation is performed to use the new feature.

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