I am using R 1.3.0 on Windows 2000. For an experiment, I am wanting to find the most diverse 400 items to study in a possible 3200 items. Diversity here is based on a few hundred attributes. For this, I would like to do a clustering analysis and find 400 clusters (i.e. different from each other in some way hopefully). From each of these 400 clusters, I will pick a representative. I expect many of these clusters will have just one item. I am planning to do this using a variety of different clustering methods. What I am wondering is if there is any way to retrieve from hclust the cluster membership after I cut off the tree. That is, if I cut the tree to segregate into my 400 clusters, is there any way to find which item goes into which cluster ... similar to the way kmeans returns "cluster"? Many thanks, M. Mark Robinson (m.robinson at utoronto.ca), M.Sc. Statistician, Best Institute, University of Toronto -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
On Wed, 1 Aug 2001, Mark Robinson wrote:> I am using R 1.3.0 on Windows 2000. > > For an experiment, I am wanting to find the most diverse 400 items to > study in a possible 3200 items. Diversity here is based on a few > hundred attributes. For this, I would like to do a clustering analysis > and find 400 clusters (i.e. different from each other in some way > hopefully). From each of these 400 clusters, I will pick a > representative. I expect many of these clusters will have just one > item. I am planning to do this using a variety of different clustering > methods. > > What I am wondering is if there is any way to retrieve from hclust the > cluster membership after I cut off the tree. That is, if I cut the tree > to segregate into my 400 clusters, is there any way to find which item > goes into which cluster ... similar to the way kmeans returns "cluster"?Have you tried this? Hierarchical clustering on 3200 items takes quite a lot of memory, so I hope you have lots. cutree will cut a tree into k(=400) clusters, and return a vector of group membership just like kmeans. -- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272860 (secr) Oxford OX1 3TG, UK Fax: +44 1865 272595 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Dear R users: is there a package, similar to varclus in SAS or varclus in S, ported or written for R? Also, is there any other package in R that was designed for grouping the variables under different measures of distance (in cases where data is non-Gaussian, autocorrelated, and so on). Janusz. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._