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From: Ted Dunning <ted.dunning@gmail.com>
Date: Thu, 12 Aug 2010 15:34:09 -0700
Message-ID: <AANLkTi=2eD+8Og=J1ztc06vG1HTGKj_e7TdCAA-67VjF@mail.gmail.com>
Subject: Re: MeanShift Clustering Patch
To: dev@mahout.apache.org
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This is a great thing in general, and we were just discussing how the
clustering and classification API's need to be made more coherent.

One thing that I particularly want to have at the end of that exercise is to
have clusters and classification models be unified.  It should not matter
(much) where a model came from, you should be able to classify new examples
using it.  You should also be able to save and restore the model in a pretty
uniform way.

This also implies that we need a consistent way to represent examples to be
classified.

What you are talking about so far is to make the construction of clustering
models more consistent which is really, really good and important, but it
needs to be in the large context of making clustering and classification
coherent as well.

What thoughts do you have on larger scale design issues?  What would you
like to see?  Can you share some user stories about how you would like to
use clustering?

On Thu, Aug 12, 2010 at 3:08 PM, Chris Wailes <chris.wailes@gmail.com>wrote:

> Lastly, I made an API change so that the MeanShiftClusterer behaved in a
> more OO fashion.  Now, instead of having a static method
> MeanShiftClusterer.clusterPoints() that then creates a MeanShiftClusterer
> object, there is an instance method called cluster().  It uses the same
> code, but makes re-use a lot easier if you want to cluster several groups of
> points using the same parameters.
>

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