[ml] clustering, weka
mike at mindmech.com
Wed May 26 04:42:51 UTC 2010
Erin, Theo, any way I could get ahold of a subset of the orthogonalized
dataset before tomorrow's meeting?
On Tue, May 25, 2010 at 8:51 PM, Andreas von Hessling <vonhessling at gmail.com
> it would be great if you could apply the clustering not to the raw
> datasets (which contain a lot of meaningless information), but to the
> orthogonalized dataset that Erin & Theo provided (where the
> skill/opportunity columns are split up into many features. Erin/Theo
> should have the latest version of these datasets. If these challenge
> datasets are too big for Weka, I suggest sampling some records -- I
> believe Thomas has some code for this.
> We *will* need to cluster the skills at some point to make use of the
> orthogonalized datasets.
> Looking forward to your results.
> On Tue, May 25, 2010 at 8:24 PM, Mike Schachter <mike at mindmech.com> wrote:
> > Hey everyone,
> > Been super busy since last week's meeting, but started
> > reading up on k-Means clustering and expecation-maximization,
> > in the hopes that I can use one of these techniques to start
> > clustering the KDD data.
> > Tonight I'm finally getting around to using Weka's built-in
> > clustering to see if it works with the KDD data:
> > http://weka.wikispaces.com/Using+cluster+algorithms
> > Can't promise anything in terms of results, but tomorrow I'd
> > be happy to give a (very) brief overview of k-means clustering
> > and expectation maximization, and hopefully some preliminary
> > results with a subset of the KDD data.
> > Perhaps some of us could work together to implement a clustering
> > in map-reduce form to work on an elastic map reduce cluster! Looking
> > forward to seeing everyone tomorrow,
> > mike
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