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July 2005

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Subject:
From:
Christian Hennig <[log in to unmask]>
Reply To:
Classification, clustering, and phylogeny estimation
Date:
Tue, 26 Jul 2005 19:46:56 +0100
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Dear Jay,

in all centroid-based partitoning methods (such as k-means or k-medoids)
simply assign the new point to the closest centroid.
In (normal) mixture modelling, you can use the estimated a posteriori
probabilities for points to have been generated by the components. That's
not different from classifying points already in the data set.

Christian

On Tue, 26 Jul 2005, Jay Liu wrote:

> Dear all,
>
>
>
> Apart from how to determine the number of clusters, another difficulty
>
> in clustering (I think) is how to predict cluster memberships of new
>
> data. This is very straight forward in classification but I can't think
>
> of a single clustering method I know can do this. I guess some
>
> model-based techniques maybe can do this but frankly, I have no clue at all.
>
>
>
>
>
>
> Jay.
>
>

*** NEW ADDRESS! ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
[log in to unmask], www.homepages.ucl.ac.uk/~ucakche

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