try this (suppose mat is your matrix): hc <- hclust(dist(mat,"manhattan"), "ward") plot(hc, hang=-1) (x <- identify(hc)) # rightclick to stop cutree(hc, 3)
km<- kmeans(mat, 3) km$cluster km$centers pam(daisy(mat, metric = "manhattan"), k=3, diss=T)$clust Baoqiang Cao a écrit : >Thanks! >I tried kmeans, the results is not very positive. Anyway, thanks Jacques! >Please let me know if you have any other thoughts! > >Best regards, > Baoqiang Cao > >======= At 2006-03-29, 00:08:44 you wrote: ======= > > > >>if you want to classify rows or columns, read: >>?hclust >>?kmeans >>library(cluster) >>?pam >> >> >>Baoqiang Cao a écrit : >> >> >> >>>Dear All, >>> >>>I have a data, suppose it is an N*M matrix data. All I want is to classify >>>it into, let see, 3 classes. Which method(s) do you think is(are) >>>appropriate for this purpose? Any reference will be welcome! Thanks! >>> >>>Best, >>> Baoqiang Cao >>> >>> >>> >>>------------------------------------------------------------------------ >>> >>>______________________________________________ >>>[email protected] mailing list >>>https://stat.ethz.ch/mailman/listinfo/r-help >>>PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >>> >>> >>> >>. >> >> > >= = = = = = = = = = = = = = = = = = = = > >Baoqiang Cao >[EMAIL PROTECTED] >2006-03-29 > > > >
______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
