similar to: How to plot the dendrogram or tree for kmeans ?

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

Hello all, I am trying to create a Clustergram in R. (More about it here: http://www.schonlau.net/clustergram.html) And to produce a picture similar to what is seen here: http://www.schonlau.net/images/clustergramexample.gif I was able (more or less) to write the R code for creating the image, but there is one thing I can't seem to figure out, that is the *changing*"smudge factor"

Dear R users, I have created data for hierarchical agglomerative cluster analysis which consist of the merging pairs and the agglomeration heights, e.g. something like my.merge <- matrix(c(-1,-2,-3,1), ncol=2, byrow=TRUE) my.height <- c(0.5, 1) I'd like to plot a corresponding dendrogram but I don't know how to convert my data to achieve this. Is it possible to create a

Dear R-users, I need some help concerning the plotting of dendrograms for hierarchical agglomerative clustering. The agglomeration niveau of each step should be displayed at the branches of the dendrogram. For this I need the x/y coordinates of the branch-agglomerations of the dendrogram. The y-values are known (the heights of the agglomeration), but how can I get the x-values? > mydata

I have a data set with 6 variables and 251 cases. The people who supplied me with this data set believe that it falls naturally into three groups, and have given me a rule for determining group number from these 6 variables. If I do scaled.stuff <- scale(stuff, TRUE, c(...the design ranges...)) stuff.dist <- dist(scaled.stuff) stuff.hc <- hclust(stuff.dist)

Hello All, I'm having some trouble figuring out what the clearest way to plot my k-means clustering result on an my existing MDS. First I performed MDS on my distance matrix (note: I performed k-means on the MDS coordinates because applying a euclidean distance measure to my raw data would have been inappropriate) canto.MDS<-cmdscale(canto) I then figured out what would be my optimum

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 guys, I've been using the kmeans and hclust functions for some time now and was wondering if I could specify a custom metric when passing my data frame into hclust as a distance matrix. Actually, kmeans doesn't even take a distance matrix; it takes the data frame directly. I was wondering if there's a way or if there's a package that lets you create distance matrices from

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

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

I'm doing kmeans partitioning on a small (n=26) dataset that has 5 variables. I noticed that if I repeatedly run the same command, the cluster centers change and the cluster membership changes. Using RW1022 under Windows NT & Windows 2000 >kmeans(pottery[,1:5], 4, 20) [...snip] $size [1] 7 3 9 7 [...snip] $size [1] 7 10 4 5 [...snip] $size [1] 6 10 5 5 yields a different

Hi there I have been using R to perform kmeans on a dataset. The data is fed in using read.table and then a matrix (x) is created i.e: [ mat <- matrix(0, nlevels(DF$V1), nlevels(DF$V2), dimnames = list(levels(DF$V1), levels(DF$V2))) mat[cbind(DF$V1, DF$V2)] <- DF$V3 This matrix is then taken and a distance matrix (y) created using dist() before performing the kmeans clustering. My query

Hey group, I have a problem of drawing dendrogram as the result of my program written in C. My algorithm is a approximation algorithm for single linkage method. AS a result I will get the following data: [Average distance] [cluster A] [cluster B] For example: 42.593141 1 26 42.593141 4 6 42.593141 123 124 42.593141 4 113 74.244206 1 123 74.244206 4 133 74.244206 1 36 So far I have used C to

Dear List RGui Version : 2.3.0 User : 1 month I am having the *dendrogram plotting problem * The code i tried: >library(cluster) DD<-DataSetS01022 # 575 x 2 matrix > VC<-hclust(dist(DD),"ave") *Warning message: NAs introduced by coercion* ( what does it mean? Is that the problem?) > plot(VC,hang=-2) Output: http://roughjade.blogspot.com Can anyone guide me? Thanks

I want to classify bipolar neurons in human cochleas and have data of the following structure: Vol_Nuc Vol_Soma 1 186.23 731.96 2 204.58 4370.96 3 539.98 7344.86 4 477.71 6939.28 5 421.22 5588.53 6 276.61 1017.05 7 392.28 6392.32 8 424.43 6190.13 9 256.41 3850.51 10 249.17 3118.14 11 276.97 3037.29 12 295.30 3703.76 13 314.43 5265.97 14 301.15 5781.73 I

Hi all, This is the first time I'm writing to R-devel, and this time I'm just asking for the purpose for a certain line of code in stats::as.hclust.dendrogram, which comes up as I'm trying to fix dendextend. The line in question is at line 128 of dendrogram.R in R-3.3.3, at stats::as.hclust.dendrogram: stopifnot(length(s) == 2L, all( vapply(s, is.integer, NA) )) Is there any

Dear I am using the 'as.dendrogram' function from the 'stats' library to convert from an hclust object to a dendrogram with a dataset of size ~30000 (an example code is below). I need the dendrogram structure to use the "dendrapply" and "attributes" functions and to access the child nodes, I do not need any of the plot properties. The problem is that it

Hi I have a distance matrix which is computed by user defined method. I would like to plot the dendrogram. I would like to use different color and want the leaves laying down bottom. The script like this. I am not familiar with R. I followed the example shown in http://stat.ethz.ch/R-manual/R-devel/library/stats/html/dendrogram.html dist.obj <- as.dist(matrix.distance) hc.obj <-

Hello, a. when I use hclust with the methods media, centroid, and mcquitty, and plot the results, the dendrograms have lines that are crossing each other. Is this ok? b. My next question refers to plot.dendrogram: How can I use parameters as "hang" or "cex" here? E.g. for st <- as.dendrogram(subtreeshc[[x]]) I would like to have something like this, where cex and hang

Hello, I have been using the "cophenetic" function for objects of class "dendrogram" and I have realised that it gives different results when it is used with objects of class "hclust". For instance, running the first example in the help file of the "cophenetic" function, d1 <- dist(USArrests) hc <- hclust(d1, "ave") d2 <-