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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Good centroid generation algorithm for top-down clustering approach
Date Tue, 26 Nov 2013 16:49:31 GMT
No.  Streaming k-means builds a small version of your data so that you can
use ball k-means or any other clever memory-resident centroid generation
algorithm.

See

http://www.cs.ucla.edu/~rafail/PUBLIC/76.pdf

http://books.nips.cc/papers/files/nips24/NIPS2011_1271.pdf




On Tue, Nov 26, 2013 at 8:36 AM, Chih-Hsien Wu <chjasonwu@gmail.com> wrote:

> I've heard about it but not familiar with it. Does Streaming K generate a
> list of centroids for other clustering algorithm?
>
>
> On Tue, Nov 26, 2013 at 10:55 AM, Ted Dunning <ted.dunning@gmail.com>
> wrote:
>
> > Have you looked at the streaming k-means work?  The basic idea is that
> you
> > generate a sketch of the data which you can then cluster in-memory.  That
> > lets you use very advanced centroid generation algorithms that require
> lots
> > of processing.
> >
> >
> >
> >
> > On Tue, Nov 26, 2013 at 6:29 AM, Chih-Hsien Wu <chjasonwu@gmail.com>
> > wrote:
> >
> > > Hi all, I'm trying to clustering text documents via top-down approach.
> I
> > > have experienced both random seed and canopy generation, and have seen
> > > their pros and cons. I realize that canopy is great for not known exact
> > > cluster numbers; nevertheless, the memory need for canopy is great. I
> was
> > > hoping to find something similar to canopy generation and was wondering
> > if
> > > there is any other recommendation?
> > >
> >
>

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