Hello Andy, the best paper that contains the pseudo-algo description and also references to the original one is this one: "Automatic Analysis of Malware Behaviour using Machine Learning" Page 9, Section 2.3.1 and Page 10, 2.3.2 The code implementation is in C and is available as part of the Malheur code base which I am familiar with. It shouldn't be that hard to implement in Python, so let me know. I am quite happy to attempt an implementation.
Regards. ________________________________________ From: Andy [[email protected]] Sent: 07 November 2013 06:06 To: [email protected] Subject: Re: [Scikit-learn-general] Prototype Extraction algorithm implementation Hi Paolo. Could you please give a link to the reference paper? I couldn't find it. Could you maybe also give a quick description of the algorithm, I'm afraid I'm not familiar with it (by that name). Thanks, Andy On 11/06/2013 07:05 AM, Paolo Di Prodi wrote: > Hello there, > I am a new user of Scikit and I was wondering if somebody had or is willing > to implement the prototype extraction algorithm (Gonzales 1985) which is a > linear time > algorithm very useful for incremental data set clustering. > In case this algorithm is not implemented should I go ahead and do a similar > implementation to sklearn.cluster.KMeans ? > The idea is then to use clustering using the extracted prototypes using > complete linkage which I think is implemented already? > > Regards. > ------------------------------------------------------------------------------ > November Webinars for C, C++, Fortran Developers > Accelerate application performance with scalable programming models. Explore > techniques for threading, error checking, porting, and tuning. Get the most > from the latest Intel processors and coprocessors. See abstracts and register > http://pubads.g.doubleclick.net/gampad/clk?id=60136231&iu=/4140/ostg.clktrk > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ November Webinars for C, C++, Fortran Developers Accelerate application performance with scalable programming models. Explore techniques for threading, error checking, porting, and tuning. Get the most from the latest Intel processors and coprocessors. See abstracts and register http://pubads.g.doubleclick.net/gampad/clk?id=60136231&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ November Webinars for C, C++, Fortran Developers Accelerate application performance with scalable programming models. Explore techniques for threading, error checking, porting, and tuning. Get the most from the latest Intel processors and coprocessors. See abstracts and register http://pubads.g.doubleclick.net/gampad/clk?id=60136231&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
