similar to: Ward clustering problem

Hi There, I'm working on some cluster analyses on a large data-set using hclust with Wards method and Manhattan (city block) distance measures. I've created dendrograms to illustrate the clustering criteria, but would like to create a plot to examine for the classic elbow criterion to use in determining the best number of clusters. Ideally I'd like to plot percent variance explained

Hi As a beginer with R I have been trying to plot dendrograms for individual groups after using cutree. The example in the help files appears to work fine for Euclidean distances using the "average" clustering method. However, when I use the "Ward" method the the reprocessed subgroup does not appear to have the same structure as it did when the whole dataset was processed. Is

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

Hi everybody, I have a problem with a cluster analysis. I am trying to use hclust, method=ward. The Ward method works with SQUARED Euclidean distances. Hclust demands "a dissimilarity structure as produced by dist". Yet, dist does not seem to produce a table of squared euclidean distances, starting from cosines. In fact, computing manually the squared euclidean distances from cosines

Hi, I have a gene expression experiment with 20 samples and 25000 genes each. I'd like to perform clustering on these. It turned out to become much faster when I transform the underlying matrix with t(matrix). Unfortunately then I'm not anymore able to use cutree to access individual clusters. In general I do something like this: hc <- hclust(dist(USArrests), "ave")

Hello list, the following approach did not work: clustersA <- pam(distances, nkA, diss=TRUE); gc(); filenameclu = paste("filenameclu", ".txt"); write.table(clustersA , file=filenameclu,sep=","); although it worked with clustersA <- hclust(distances, method="ward"); and a consecutive kclassA <- cutree(clustersA, k=nkA); filename =

Hi! I just discovered that cutree() and cut.dendrogram() do not assign the same cluster numberings when called on the same tree. More specifically, cutree() assigns cluster numbers by order of appearance in the data, while cut.dendrogram() sorts clusters by height (see example below). I guess this is for historical reasons? I'm hit by this difference when I want to get a vector of cluster

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi, I use the following clustering methods and get the corresponding dendrograms for single, complete, average, ward and kmeans clustering. This gives the dendrograms, but doesn't show the calculation-way. My question: is there a possibility to show this calculation steps (cluster steps) in matrix or graphical form? Mit freundlichen Gr??en Ralph Modjesch

Dear R user, I am a biochemist/bioinformatician, at the moment working on protein clusterings by conformation similarity. I only started seriously working with R about a couple of months ago. I have been able so far to read my way through tutorials and set-up my hierarchical clusterings. My problem is that I cannot find a way to obtain information on the rooting of specific nodes, i.e. of

The R function hclust is used to do cluster analysis, but based on R help I see no way to print the actual fusion distances (that is, the vertical distances for each connected branch pairs seen in the cluster dendrogram). Any ideas? I'd like to use them test for significant differences from the mean fusion distance (i.e. The Best Cut Test). To perform a cluster analysis I'm using: x

Dear WizaRds, My goal is to program the VS-KM algorithm by Brusco and Cradit 01 and I have come to a complete stop in my efforts. Maybe anybody is willing to follow my thoughts and offer some help. In a first step, I want to use a single variable for the partitioning process. As the center-matrix I use the objects that belong to the cluster I found with the hierarchial Ward algorithm. Then,

Hello to the R people, within ward clustering the distance calculated to decide the clustering of 2 subsets (h1 and h2) is the variation of inertia : d(h1,h2)=I(h1Uh2)-I(h1)-I(h2); i've been said that a way to calculate faster this d(h1,h2) is using the huygens theorem decomposing the inertia into "the inertia to the centroid + the distance to an axe" (that's my version ...). My

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

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.

Hi R people, i'm looking for the Reciprocal Nearest Neighbour algorithm applied to Ward clustering; I know that it is explained in details in the following book: Downs, G. M.; Willett, P. In Advanced Computer-Assisted Techniques in Drug Discovery; van de Waterbeemd, H., Ed.; VCH: Weinheim, 1994; Vol. 3. but cannot order copies it without knowing the pages involved; could someone

Hello everybody, I Would like to get some help to plot together, Principal Components Analysis (PCA) and clusters. I am handling environmental data from 25 locations spread across 5 different ecosystems.When grouped into 5 clusters, locations from different ecosystems are arranged in the same group. So, I want to plot together PCA and Clusters, in a such way that locations belonging to the same

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

> I have performed clustering analysis with hclust (Ward's method) on a > database of 800 samples. As you may imagine the full dendrogram is not > really readable. I have obtained groups with cutree. I would like to plot > sub-sections of my big dendrogram to show group 1, group 2 and so on. I don't think R has anything like subtree in Splus, unfortunately. I think what has