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https://issues.apache.org/jira/browse/MAHOUT-236?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12859040#action_12859040
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Ted Dunning commented on MAHOUT-236:
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Typically any place where you have an algorithm that assumes a hard-membership,
but what you have is a soft membership clustering algorithm, you can just pick
the cluster with the strongest membership signal. You don't need a threshold.
Conversely, in applications where you need soft membership and have hard
membership, you should insert (1-epsilon) for the one cluster the document is
in and epsilon/(k-1) for the other k-1 clusters. Epsilon should be tuned for
best results on a corpus but should generally not be zero.
> Cluster Evaluation Tools
> ------------------------
>
> Key: MAHOUT-236
> URL: https://issues.apache.org/jira/browse/MAHOUT-236
> Project: Mahout
> Issue Type: New Feature
> Components: Clustering
> Reporter: Grant Ingersoll
> Attachments: MAHOUT-236.patch
>
>
> Per
> http://www.lucidimagination.com/search/document/10b562f10288993c/validating_clustering_output#9d3f6a55f4a91cb6,
> it would be great to have some utilities to help evaluate the effectiveness
> of clustering.
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