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From "Beniamino (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-2344) Add Fuzzy C-Means algorithm to MLlib
Date Wed, 11 Mar 2015 11:29:39 GMT

    [ https://issues.apache.org/jira/browse/SPARK-2344?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14356741#comment-14356741
] 

Beniamino commented on SPARK-2344:
----------------------------------

Hi,

yes the computation of the next centers are made on the fly avoiding to store the membership
matrix. 

The algorithm already works; the only thing that might be added is the run parameter such
as the K-Means' implementation.

I've already done the Fukuyama Sugeno validity index computation too.

Beniamino

> Add Fuzzy C-Means algorithm to MLlib
> ------------------------------------
>
>                 Key: SPARK-2344
>                 URL: https://issues.apache.org/jira/browse/SPARK-2344
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Alex
>            Priority: Minor
>              Labels: clustering
>   Original Estimate: 1m
>  Remaining Estimate: 1m
>
> I would like to add a FCM (Fuzzy C-Means) algorithm to MLlib.
> FCM is very similar to K - Means which is already implemented, and they differ only in
the degree of relationship each point has with each cluster:
> (in FCM the relationship is in a range of [0..1] whether in K - Means its 0/1.
> As part of the implementation I would like:
> - create a base class for K- Means and FCM
> - implement the relationship for each algorithm differently (in its class)
> I'd like this to be assigned to me.



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