similar to: k means

Not sure who to send this to... On the books page http://www.r-project.org/doc/bib/R-books.html the entries from [54] down have three forward slashes in the hyperlinks to publisher info and so they don't work Robin [[alternative HTML version deleted]]

Hi - Writing to see if someone can suggest whether a problem warrants a bug report. It concerns the use of stepFlexclust in the flexclust package. The problem concerns the size of clusters returned. Versions: R-2.7.0 on Windows XP; RODBC_1.2-3 code snippet: r8 <- stepFlexclust(df,8,nrep=100,FUN=kcca, family=kccaFamily("kmedians")) summary(r8) ## returns cluster sizes of 51, 115,

Dear all, I have just found a 'good' tutorial R for datamining. I think it should be on the contributed docs. http://cran.r-project.org/other-docs.html Here is the link http://www.liaad.up.pt/~ltorgo/DataMiningWithR/ What do you think? Kind regards Christophe -- Christophe Dutang Ph. D. student at ISFA, Lyon, France website: http://dutangc.free.fr

On Thu, Jul 23, 2009 at 3:04 PM, Duncan Murdoch<murdoch at stats.uwo.ca> wrote: > On 23/07/2009 8:58 AM, Rainer M Krug wrote: >> >> Hi >> >> I hope this is the right mailing list - if not, could you please refer >> me to a mora appropriate? >> >> My question: >> >> I am using sweave (in LyX with beamer) for a lecture and I would like

Dear R users, When I run the following code, R crashes: require(cclust) x <- matrix(c(0,0,0,1.5,1,-1), ncol=2, byrow=TRUE) cclust(x, centers=x[2:3,], dist="manhattan", method="kmeans") While this works: cclust(x, centers=x[2:3,], dist="euclidean", method="kmeans") I'm posting this here because I am not sure if it is a bug. I've been searching

Dear List, I'd like to set up a package repository so I can use install.packages() on it for home-grown packages. I set up an AMPP infrastructure on a windows box already, but I'm pretty lost with respect to what to do next as I didn't do any web-programming/admin yet. Could anyone recommend some r-specific tutorials or has a couple of suggestions for me? I've had a look at the

Hi All, For the function "bclust"(e1071), the argument "base.method" is explained as "must be the name of a partitioning cluster function returning a list with the same components as the return value of 'kmeans'. In my understanding, there are three partitioning cluster functions in R, which are "clara, pam, fanny". Then I check each of them to

Hi, in approximately one months time mentoring institutions can propose projects for the Google Summer of Code 2009, see http://code.google.com/soc/ Last year the R Foundation succesfully participated with 4 projects, see http://www.r-project.org/SoC08/ for details. We want to participate again this year. Our project proposals will be managed by Manuel Eugster (email address in CC). Manuel

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 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 was wondering if there was an R equivalent to the two phased approach that MATLAB uses in performing the Kmeans algorithm. If not is there away that I can determine if the kmeans in R and the kmeans in MATLAB are essentially giving me the same clustering information within a small amount of error? -- View this message in context:

Dear members. I am having problems to understand the kmeans- results in R. I am applying kmeans-algorithms to my big data file, and it is producing the results of the clusters. Q1) Does anybody knows how to find out in which cluster (I have fixed numberofclusters = 5 ) which data have been used? COMMAND (kmeans.results <- kmeans(mydata,centers =5, iter.max= 1000, nstart =10000)) Q2) When I

Hello, I am hoping you can help me with a question concerning kmeans clustering in R. I am working with the following data-set (abbreviated): BMW Ford Infiniti Jeep Lexus Chrysler Mercedes Saab Porsche Volvo [1,] 6 8 2 8 4 5 4 4 7 7 [2,] 8 7 4 6 4 1 6 7 8 5 [3,] 8 2 4

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]

*Apologies if this is not the right way to ask a question, I'm a first timer posting here. Does anyone have a solution to this? I'm having trouble figuring out how to use weighting with K Means Clustering. So say if my dataset is: Column 1 = x coords Column 2 = y coords Column 3 = frequency each coordinate occurs So I'm basically trying to weight the points more heavily if

Dear R experts, we are preparing an R-package to compute the Oja Median which contains some C++ code in which random numbers are needed. To generate the random numbers we use the following Mersenne-Twister implementation: // MersenneTwister.h // Mersenne Twister random number generator -- a C++ class MTRand // Based on code by Makoto Matsumoto, Takuji Nishimura, and Shawn Cokus // Richard J.

Hi All, Can someone please tell me* R function for Bisecting K-means algorithm*. I have used *kmeans() *function but not getting good results. Please help. -- Thanks and Regards, Vivek Kumar Singh Research Assistant, School of Computing, National University of Singapore Mobile:(0065) 82721535 [[alternative HTML version deleted]]

Buen día, no sé si estoy utilizando bien la lista, es la primera vez. Si lo hago mal me corrigen por favor. Sobre tu comentario Pedro, muchas gracias. Lo qeu entiendo con tu sugerencia de set.seed es qeu de esa forma fijas los resultados, pero no estoy seguro si otra agrupación funcione mejor. Es decir me interesa un método de agrupación que genere la "mejor" agrupación y como los

K-means recluster data with given cluster centers Dear R user, I have several large data sets. Over time additional new data sets will be created. I want to cluster all the data in a similar/ identical way with the k-means algorithm. With the first data set I will find my cluster centers and save the cluster centers to a file [1]. This first data set is huge, it is guarantied that cluster

Hi there, I'm doing some clustering analysis and try to find all the algorithms related to clustering in R. Here is the list of the algorithms I found. But I'm not sure if It's the complete list. Could you please check it and see if there're other ones? Thank you very much! P.S.: List of the algorithms related to clustering: (1) Hierarchical methods hclust