similar to: K-means result - variance between cluster

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

Hi there, As I could not find a direct maintainer email address, I'm sending this to the mailing list. SysLinux, being a GPL product, is being sold under a very restrictive license without even offering the sources to the public. I've written up my findings here: http://www.palli.nl/~ivo/rdp/ Please have a look for yourself. Regards, Ivo Palli

Hi, I just tried to boot my token ring devices with 2.05 - to no avail, it hangs after loading the kernel. I then tried older versions, and found 2.03 booting like a charm, and every 2.04-pre including 2.04 hanging. Here's the config of the devices: - Networkstation 2800 with IBM PCI Token Ring card 2 - IBM Thinkpad T21 with IBM PCI Token Ring card 2 Maybe this has to do with the changes

Dear R-Users, The following question is more of general nature than a merely technical one. Nevertheless I hope someone get me some answers. I have been using the mice package to perform the multiple imputations. So far, everything works fine with the standard regressions analysis. However, I am wondering, if it is theoretically correct to perform nonparametric models (GAM, spline

Hi Folks, I'm trying to test out Asterisk overall. I'm having some problems with DTMF. Currently I'm playing with DISA, but I'm worried this will happen when I get to implementing AAs etc. I have a free SIP trunk from IPKall that I'm trying to make work. I'm able to receive calls, and I've now setup and extension with DISA and a password. I connect ok from the

hi guys, first of all, i'd like to ask if there any crossplatform software/service ( gui is also better, an online application like firefogg is great, or at least a command line tools for any platform) to combine theora videos, other than oggcat... i am doing some art projects that people should be able to remix featured theora videos and i find oggcat very usefull in theory but not in

What happened to the category k-means package for performing k-means clustering on categorical variables? I expected it to become more prominent after the Netflix Challenge recommendation engine contest concluded, but instead it seems to have dropped from view. Where was it and where is it now, and why did it become less visible?

I am trying to call a C++ k-means function within R and I am struggling. I know that the below code is used to call a C++ function for gbm but how do I do it for k-means? gbm.obj <- .Call("gbm", Y=as.double(y), Offset=as.double(offset), X=as.double(x), X.order=as.integer(x.order),

I am running a k-means clustering code in R : mydata_kmeans5 <- kmeans(mydata, centers=5).. But the problem is that the data is having some "NA" in it. So R is showing me a message :Error in switch(nmeth, { : NA/NaN/Inf in foreign function call (arg 1) In addition: Warning messages: 1: In switch(nmeth, { : NAs introduced by coercion 2: In switch(nmeth, { : NAs introduced by

Hi, i have the following problem: I am using k-means algorithm for clustering. But instead of using randomized centers, I would like to use centers created by hierarchical clustering. So I want to apply "hclust" on my data set (in this case the iris data), getting a solution by "cutree", calculating the means/centers of the resulting clusters and use these centers as starting

Hi all, Simple question re k-means. If I have a data set with columns that are on different scales (say col 1 has var=100 and col2 var=2), will this make a difference to the k-means algorithm? It seems as though it does. If so, should we first standardize the columns of the dataset so that each column is given equal weight? JJ [[alternative HTML version deleted]]

Hi, I need some answers regarding to R. In K-means clustering ,for K=2, How can I find the members of each cluster and What are the coordinates for the cluster centers? Please send me a reply soon. Thank You Sent from Windows Mail [[alternative HTML version deleted]]

Hello, the k-means {stats} help and the Hartigan&Won paper say nothing about the way random sets works (parameter nstart). I would expect to get the different results for each random initial set but I always obtain only one result: how is it selected? Charles Raux

Hi all, I am trying to run the k-means cluster analysis using the function kmeans in the package cluster. The data are: x = c(-0.26, -0.23, -0.05, -0.20, 0.30, -0.84, -0.10, -0.12, 0.10, -0.31, -0.19, 0.18, -0.26, -0.23, -0.37, -0.23) I've got two different solutions when I ran this function over a few times: kmeans(x, centers=2) The first solution gives the following: $cluster [1]

Hi all, I am wandering.. is it possible to cluster data which is in a single column ? for example.. I have some data as follows: 4013 7362 7585 9304 11879 14785 21795 30500 30669 30924 33988 36975 40422 42911 50501 51593 53729 54338 55497 57337 61993 62601 66229 69815 69933 70760 71340 75921 83972 90134 91061 . . . is it possible to cluster this data since it is in a single column ? I have

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Hello, I'm new using R. I'm trying to develop a K-means Clustering with R for some data I have, however each time I use that instruction with the same data my cluster means, clustering vector and within cluster sum of square change and I don't understand why because I use the same parameters and the same data. Can anybody explain me why does it happen? Thank you Act. Calef

Hi all I am working on k-means algorithm (in R: kmeans( ) ). The R-help advice us to try several random start in order to avoid local minimum. Does one know if there is a procedure that automaticly run this rerolling and select the best partition ? Or any studies that gives clues on the number of rerolling ? Thanks for helping. Christophe

Hello, I am trying to create a heatmap that clusters based on a k-means scheme rather than a hierarchical clustering scheme. Suppose I have the following input data, located in sample.table: x1 x2 x3 x4 x1 17.198 16.306 16.806 16.374 x2 14.554 10.866 15.780 14.596 x3 14.374 14.118 14.569 17.352 x4 17.505 14.596 15.738 14.070 By using the heatmap.2 function as follows, I can create a heatmap

Dear R user, How can I get the C or C++ source code of the "R_kmeans_MacQueen" or "R_kmeans_Lloyd" subroutines implemented in the R "kmeans" function of the R stats-package {stats}? Would these be available as a C header file (*.h) somewhere from the R installation??? Any help to get a C or C++ implementation of these clustering algorithms would be very