similar to: cutree with agnes

Hi there, is there any R-function for k-nearest neighbour agglomerative hierarchical clustering? By this I mean standard agglomerative hierarchical clustering as in hclust or agnes, but with the k-nearest neighbour distance between clusters used on the higher levels where there are at least k>1 distances between two clusters (single linkage is 1-nearest neighbour clustering)? Best regards,

Thanks a lot, Michael! I cc to R-help, where this question really belongs {as the 'Subject' suggests itself...} -- please drop 'bioconductor' from CC'ing further replies. >>>>> "michael" == michael watson (IAH-C) <michael.watson at bbsrc.ac.uk> >>>>> on Thu, 17 Jun 2004 09:16:59 +0100 writes: michael> OK, admittedly it

Hello all, A question/suggestion: I was wondering if there is a chance of changing stats::cutree to be S3 and use cutree.hclust? For example: cutree <- function(tree, k = NULL, h = NULL,...) { UseMethod("cutree") } cutree.hclust <- stats::cutree # This will obviously need the actual content of stats::cutree This would be nicer for people like me to add new methods to

Well, you don't give much of an example.... I'm replying CC to the R mailing list. Please ask questions there, rather than adressing individuals for basic help. Here is one; does it answer your question ? data(agriculture) ag.ag <- agnes(agriculture) class(ag.ag) pltree(ag.ag) # the dendrogram, if you want to see it ## cut the dendrogram -> get cluster assignments: (ck3 <-

Hello, I would like to cut a hclust tree into several groups at a specific similarity. I assume this can be achieved by specifying the "h" argument with the specified similarity, e.g.: clust<-hclust(dist,"average") cut<-cutree(clust,h=0.65) Now, I would like to draw rectangles around the branches of the dendrogram highlighting the corresponding clusters, as is done by

Hi, I have the distance matrix computed and I feed it to hclust function. The plot function produces a dense dendrogram as well. But, the cutree function applied does not produce the desired list. Here is the code x=data.frame(similarity_matrix) colnames(x) = c(source_tags_vec) rownames(x) = c(source_tags_vec) clust_tree=hclust(as.dist(x),method="complete") plot(clust_tree)

I've found that while cutree() and rect.hclust() make the same classes for a given height in the dendrogram, the actual labeling of the classes is different. For example, both produce the same 4 classes but class 1 according to cutree() is class 4 according to rect.hclust(). Would it be possible that future versions provide the same labeling? rect.hclust() is useful to display the classes

I'm sorry if this is a trivial question. My system is R 6.33 on Win98 with basic libraries plus the "cluster" library. I'm using "agnes" and "hclust" functions to perform cluster analysis on big data sets. Looking at the documentation for libraries "mva" and "cluster" I can't find out a function like S-Plus "cutree" to get

I'm clustering rather large data sets and would like to cut the dendrograms to get a better view of specific components. I calculate the dissimilarity matrix using daisy() because I have a mixture of variable types: factors, ordered factors and numerical variables. If I want one dendrogram, I use agnes() for the agglomerative nesting and pltree() to draw the dendrogram. That way, I get the

Hi Everyone, I'm trying to use cutree to get the clusters after hclust. What I used is: mycluster<-cutree(cnclust,h=0.5) Now, my problem is, how can I get the actual clusters? Thanks! Best, Baoqiang Cao

Hello, I had a question regarding clustering using the agnes() function from the 'cluster' package. I was wondering if anyone knew how I can identify cluster points after running the agnes function. For example, I created a dataset with points randomly scattered around (0,0), (0,1) and (1,0). After clustering, the dendrogram shows all the clustered points and I get the ordering and

Good afternoon, After cuting a hierarchical tree using cutree(), how to check correspondances between classes and branches? This is what we do: srndpchc <- hclust(dist(srndpc$x[1:1000,1:3]),method="ward") #creation of hierarchical tree plclust(srndpchc,hmin=20000) #visualisation srndpchc20000 = cutree(srndpchc,h=20000) #returns 4 classes table(srndpchc20000 ) srndclass20000 =

I have the following code that perform hiearchical clustering and plot them in heatmap. __ library(gplots) set.seed(538) # generate data y <- matrix(rnorm(50), 10, 5, dimnames=list(paste("g", 1:10, sep=""), paste("t", 1:5, sep=""))) # the actual data is much larger that the above # perform hiearchical clustering and plot heatmap test <- heatmap.2(y)

Full_Name: Anja von Heydebreck Version: 1.3.0 OS: Alpha Unix Submission from: (NULL) (141.14.19.61) Hi, I repeatedly obtained meaningless results from the function 'cutree' in the 'mva' package, when the argument 'h' was greater or equal to the maximum height occuring: > library('mva') > y [,1] [,2] [,3] [,4] [1,] 0 1 -1 1 [2,] 0 -1

I try to use the routine cutree to cut a tree (created by hclust) into several groups by specifiing the hight of the cut. a<-1:10 cutree(tree, h=a*100) The Matrix with group meberships returned is ok for most of the hights, but in some cases (as for example h=800 and h=900) the results don't make sense (group membership=0 or 58965231, it looks like the range of data allowed by the data

I have Windows XP Professional Version 2002 and the R-Version 2.1.1. I did cluster analysis with the cluster package and the agnes (method = ?ward?). The results are satisfactory. But the dendrogram of agnes is confused to work with the results. Is there a tool, I can get a clear arrangement of the results for the cluster analysis. For example a matrix with different numbers for each group.

Dear R users, I discovered something strange using the function agnes() of the cluster package on R 3.0.1 and on R 2.14.1. Indeed, the clusterings obtained are different whereas I ran exactly the same code. I quickly looked at the source code of the function and I discovered that there was an important change: agnes() in R 2.14.1 used a FORTRAN code whereas agnes() in R 3.0.1 uses a C code.

Dear R-help listers, Is anyone aware of a function that outputs dendrogram ultrametrics? Cheers, Lisa. PS please reply to me personally as well as to the list because the website wasn't letting me subscribe for some reason. thanks... Lisa Holman Research Officer, Vegetation Dynamics Policy & Science Division NSW Department of Environment & Conservation PO Box 1967, Hurstville 2220.

Hello, I've been clustering my data using hclust and cutting the resulting tree with cutree. Separately, I visualize the clusterings with heatmap. Is it possible to have the dendrogram on the heatmap reflect the cutree results? That is, instead of having one large dendrogram, it would have 4 or 25 in the example below. Any guidance on if that's possible or not, and what kinds of

Hello, I am using the dunn metric, but something is wrong and I dont understand what or what that this error mean. Please can you help me with this? The instructions are: #Indice de Dunn disbupa=dist(bupa[,1:6]) a=hclust(disbupa) cluster.stats(disbupa,a,bupa[,7])$dunn And the error is: Error in max(clustering) : invalid 'type' (list) of argument thank you so much. Ana Maria