Okay, then I'll put together a biclustering proposal tomorrow after work.
It will be a difficult task to come up with a good set of core algorithms,
because the field is so varied. There are over a hundred published methods,
each of which formulates the biclustering problem differently. Any
particular algorithms you would like to see in scikit-learn?
Best,
Kemal
On Thu, Apr 25, 2013 at 3:58 AM, Vlad Niculae <[email protected]> wrote:
> Exactly, I was talking about predict and about the state of the
> estimator. It seemed much more difficult before I thought about it
> better :)
>
> On Thu, Apr 25, 2013 at 10:54 AM, Mathieu Blondel <[email protected]>
> wrote:
> >
> > On Thu, Apr 25, 2013 at 10:26 AM, Vlad Niculae <[email protected]>
> wrote:
> >>
> >> If we are talking about the same thing, you are returning clusters of
> >> samples and features together (ie rows and columns). So if in K-means
> >> we return a 1D array with cluster labels, here the output would be two
> >> arrays, one of (n_samples,) and one of (n_features,). Another
> >> alternative would be a list of length `n_clusters` where each element
> >> is a pair of lists of row, respectively column indices. But I believe
> >> the first one can be uniform enough wrt our current API.
> >
> >
> > I think you are talking about the predict method. In the case of the fit
> > method, I think we only need fit(X). Then the fitted attributes could be
> > row_clusters_ where row_clusters_[i, k] = 1 means that the row i belongs
> to
> > cluster k and col_clusters_ where col_clusters_[j, k] = 1 means that
> column
> > j belongs to cluster k.
> >
> > Mathieu
>
>
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