Hi Kemal,
Below a few comments.
- Please describe in more details what is biclustering and why it is
important. A figure would be nice.
- Can you indicate the citation count of the reference papers you mention?
The higher, the better...
- Binarization is already supported in scikit-learn.
- Missing value imputation is also part of Nicolas Trésegnie's proposal on
matrix completion. You guys should talk.
- nimfa is a GPL package: you cannot reuse this code in scikit-learn.
- Elaborate the data generation and evaluation parts and why you need 3
weeks for that.
- Give a code snippet to show how using your implemented algorithms would
look like (this will also demonstrate compatibility with our APIs).
- Tests and examples should be written at the same time as you implement
the algorithms (not at the end of the GSOC).
Mathieu
On Sun, Apr 28, 2013 at 10:40 PM, Kemal Eren <ke...@kemaleren.com> wrote:
> Hi scikit-learn team,
>
> As discussed in another thread, I have put together a draft of my proposal
> to add biclustering algorithms this summer. Here is the link:
>
>
> https://docs.google.com/document/d/17v1r5gqnMA6ExmYYxOSBWjfAdQiCc7-Gjm4O_8QtTJs/edit?usp=sharing
>
> Any suggestions are welcome.
>
> Best regards,
> Kemal Eren
>
>
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