Hi, if you use the function kmean in the package stats, for example clust <- kmeans(data, k, iter.max = 10)
where k is the number of desired cluster, kmeans will choose the first k centers randomly. Because of this random initialization, after iter.max iteration the solution may converge to different final clusters (and therefore different centers and validity measures). see ?kmeans and look at the parameter 'centers'. Roberto http://roberto.perdisci.googlepages.com On Nov 14, 2007 6:07 PM, Alejandro Rodríguez <[EMAIL PROTECTED]> wrote: > > Hello, I'm new using R. > > I'm trying to develop a K-means Clustering with R for some data I have, > however each time I use that instruction with the same data my cluster > means, clustering vector and within cluster sum of square change and I don't > understand why because I use the same parameters and the same data. > > Can anybody explain me why does it happen? > > Thank you > > > > Act. Calef Alejandro Rodríguez Cuevas > Analista de mercado > > Laboratorios Farmasa S.A. de C.V. > Schwabe Mexico, S.A. de C.V. > > Bufalo Nr. 27 > Col. del Valle 03100 > Mexico, D.F. > Mexico > > Tel. 52 00 26 80 > email: [EMAIL PROTECTED] > > www.schwabe.com.mx > www.umckaloabo.com.mx > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
