Thanks Andreas. I will be implementing them for my own work anyway, and will add the necessary stuff required to pass the quality standards and send it around for approval. I am not sure how long it will take, but was asking this to align myself with the requirements while working on my stuff.
In any case, if you think it is useful (with any necessary revisions), you can pull it in. No worries otherwise. When you said robust did you mean it in the robust-statistics sense (say, noise tolerant version) or just a definitive algorithm? The algorithm itself is pretty well defined (say the classical one from Hartigain's book, or SLINK with Sibson's improvements) , with similarity metrics and merging strategies passed as the input, along with the data. Best, Pavan On Mar 4, 2013, at 2:00 PM, Andreas Mueller <amuel...@ais.uni-bonn.de> wrote: > Hi Pavan. > There are no hierarchical algorithms beside WARD. > It would indeed be great to have single-link and complete link. > Is there any robust version of single-link btw? What description would > you go by? > > As always, the disclaimer: Getting a new algorithm into scikit-learn is > a bit more than > writing down the algorithm. You need to provide tests and documentation > and need > to respect our coding guidelines. So that usually involves a bit of work. > > On the other hand, having this classical algorithms in sklearn would > definitely be great! > > Cheers, > Andy > > > On 03/04/2013 08:46 PM, Pavan Mallapragada wrote: >> Hi, >> >> I am trying to find the single link / complete link algorithms in >> scikit-learn. I see Ward's is the only hierarchical clustering algorithm >> implemented (from the documentation). >> >> I did find other extensions of scipy implementing these, e.g. hcluster >> (http://code.google.com/p/scipy-cluster/). >> >> I am willing to contribute by adding the implementations of other >> hierarchical clustering algorithms. I am emailing to check if I am missing >> something before trying to add my implementations. >> >> Thanks, >> Pavan >> ------------------------------------------------------------------------------ >> Everyone hates slow websites. So do we. >> Make your web apps faster with AppDynamics >> Download AppDynamics Lite for free today: >> http://p.sf.net/sfu/appdyn_d2d_feb >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > ------------------------------------------------------------------------------ > Everyone hates slow websites. So do we. > Make your web apps faster with AppDynamics > Download AppDynamics Lite for free today: > http://p.sf.net/sfu/appdyn_d2d_feb > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_d2d_feb _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
