Dear Michael, Yes, AFAIK you are correctly reading the results. You can print elbow.obj$k to obtain the optimal number of clusters, and ‘visually’ you can check it plotting the variance vs #clusters plot(css.obj$k, css.obj$ev)
HTH Best, Luisfo Chiroque PhD Student IMDEA Networks Institute http://fourier.networks.imdea.org/people/~luis_nunez/ <http://fourier.networks.imdea.org/people/~luis_nunez/> > El 12 abr 2016, a las 4:30, Michael Artz <michaelea...@gmail.com> escribió: > > Hi, > I already have a dissimilarity matrix and I am submitting the results to > the elbow.obj method to get an optimal number of clusters. Am I reading > the below output correctly that I should have 17 clusters? > > code: > top150 <- sampleset[1:150,] > {cluster1 <- daisy(top150 > , metric = c("gower") > , stand = TRUE > , type = list(symm = 1)) > } > > dist.obj <- dist(cluster1) > hclust.obj <- hclust(dist.obj) > css.obj <- css.hclust(dist.obj,hclust.obj) > elbow.obj <- elbow.batch(css.obj) > > [1] "A \"good\" k=17 (EV=0.80) is detected when the EV is no less than > 0.8\nand the increment of EV is no more than 0.01 for a bigger k.\n" > attr(,"class") > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
