similar to: kmeans: "did not converge in 10 iterations"

Dear R users, I have some trouble with the aggregate function. Here are my data > daf S_id AF_Class count... R_gc_percent S_length 5 8264497 1 30 0.48 35678 6 8264497 3 7 0.48 35678 8 8264554 1 31 0.51 38894 9 8264554 2 11 0.51 38894 10 8264554 3 1 0.51 38894

Dear R-Users, I recently gave a try to the nice package ggplot2. Everything went well until I tried to add a smoother (using lm method for instance). On the graphic device the regression line is displayed but not confidence intervals as it should be (at least on ggplot website). I tried to do the job on both MS winXP and Linux i586: same result. Did anyone encountered this problem? Did I miss

Dear R users, I would like to query a MySQL database through R. I have installed the latest required packages (RMySQL and DBI) in R (v2.3.1). A MySQL server (v5.0.22) is running on my local machine but I can't initialize MYSQL driver: ------------------------------------------------------------------------------------ > library("RMySQL") Loading required package: DBI >

Dear all, I need to know in which number of iterations the kmeans converge each time I run it. Any idea how to do it? Thank you for your attention, Rui

Hi All, Just want to make sure, in function "bclust", do the following argument only have one option? argument "dist.method" has one option "Euclidian"; argument "hclust.method" has one option "average"; argument "base.method" has one option "kmeans". Thank you! [[alternative HTML version deleted]]

Dear R-users, is there a way to pass a list of patterns to the grep function? I vaguely remember something with %in% operator... Thanks, St?phane. -- "La science a certes quelques magnifiques r?ussites ? son actif mais ? tout prendre, je pr?f?re de loin ?tre heureux plut?t qu'avoir raison." D. Adams -- AGC website <http://www.genoscope.cns.fr/agc> St?phane

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

Dear All: I'm a newbie to R and chemometrics. Now I'm trying apply bclust on fuzzy c-means like this: >bc1 <- bclust(iris[,1:4], 3, base.centers=20,iter.base=100, base.method="cmeans") Committee Member: 1(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)Erro r in bclust(iris[, 1:4], 3, base.centers = 20, iter.base = 100, base.method =

Hello. Is it possible to choose the distance in the kmeans algorithm? I have m vectors of n components and I want to cluster them using kmeans algorithm but I want to use the Mahalanobis distance or another distance. How can I do it in R? If I use kmeans, I have no option to choose the distance. Thanks in advance, Arnau.

Hi, I wish to build consensus groups/tree from a set of bootstraps from a clustering algorithm such as hc or k-means, but can't find an R-function that does this. Does anyone know of an R procedure/function which allows one to build such consensus groups/tree .? Many thanks, Andrew ******************************************************************* Dr Andrew E Teschendorff Hutchison/MRC

Hello, I'd like to classify data with kmeans algorithm. In my case, I should get 2 clusters in output. Here is my data colCandInd colCandMed 1 82 2950.5 2 83 1831.5 3 1192 2899.0 4 1193 2103.5 The first cluster is the two first lines the 2nd cluster is the two last lines Here is the code: x = colCandList$colCandInd y = colCandList$colCandMed m = matrix(c(x, y),

Hi All, I was using the following command for performing kmeans for Iris dataset. Kmeans_model<-kmeans(dataFrame[,c(1,2,3,4)],centers=3) This was giving proper results for me. But, in my application we generate the R commands dynamically and there was a requirement that the column names will be sent instead of column indices to the R commands.Hence, to incorporate this, i tried using the R

Regarding a previous question concerning the kmeans function I've tried the same example and I also get a strange result (at least according to what is said in the help of the function kmeans). Apparently, the function is disregarding the initial cluster centers one gives it. According to the help of the function: centers: Either the number of clusters or a set of initial cluster

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

Hi, I am using kmeans to cluster a dataset. I test this example: > data<-matrix(scan("data100.txt"),100,37,byrow=T) (my dataset is 100 rows and 37 columns--clustering rows) > c1<-kmeans(data,3,20) > c1 $cluster [1] 1 1 1 1 1 1 1 3 3 3 1 3 1 3 3 1 1 1 1 3 1 3 3 1 1 1 3 3 1 1 3 1 1 1 1 3 3 [38] 3 1 1 1 3 1 1 1 1 3 3 3 1 1 1 1 1 1 3 1 3 1 1 3 1 1 1 1 3 1 1 1 1 1 1 3

Dear helpers I'm sorry to insist but I still think there is something wrong with the function kmeans. For instance, let's try the same small example: > dados<-matrix(c(-1,0,2,2.5,7,9,0,3,0,6,1,4),6,2) I will choose observations 3 and 4 for initial centers and just one iteration. The results are > A<-kmeans(dados,dados[c(3,4),],1) > A $cluster [1] 1 1 1 1 2 2 $centers

Using R 2.1.0 on Windows 2 questions: 1. Is there a way to parse the output from kmeans within R? 2. If the answer to 1. is convoluted or impossible, how do you save the output from kmeans in a plain text file for further processing outside R? Example: > ktx<-kmeans(x,12, nstart = 200) I would like to parse ktx within R to extract cluster sizes, sum-of-squares values, etc., OR save ktx in

Hi, I'm playing around with the 'bigmemory' package, and I have finally managed to create some really big matrices. However, only now I realize that there may not be functions made for what I want to do with the matrices... I would like to perform a cluster analysis based on a big.matrix. Googling around I have found indications that a certain kmeans.big.matrix() function should

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

Sorry, to solve your question I had tried: data(faithful) kmeans(faithful[c(1:20),1],10) Error: empty cluster: try a better set of initial centers But when I run this a second time it will be ok. It seems, that kmeans has problems to initialize good starting points, because of the random choose of these starting initial points. With kmeans(data,k,centers=c(...) the problem can be solved.