The book is very old actually and highly cited -- published in 1975 by J. A. Hartigan, one of those clustering books fit to be a classic (in my opinion).
Most newer books refer to this one. Pavan On Mar 4, 2013, at 2:49 PM, Andreas Mueller <amuel...@ais.uni-bonn.de> wrote: > Hi Pavan. > I meant robust to outliers. But I guess that is encoded in the merging > strategy. > I didn't know about the book. Is it any good / recent? > > Cheers, > Andy > > On 03/04/2013 09:20 PM, Pavan Mallapragada wrote: >> 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 > > > ------------------------------------------------------------------------------ > 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
