similar to: beginners question - kmeans

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-help, Please can someone explain how to put a column name on an output data.frame. ##Starting with a data.frame with 3 columns (d$Year, d$NoIndiv, d$wtd_tl) yr_ind <- split (d$NoIndiv, d$Year) yr_tl <- split (d$wtd_tl, d$Year) ann_ind <- sapply (yr_ind, sum) ann_tl <- sapply (yr_tl, sum) av_tl <- ann_tl/ann_ind d2<- data.frame (av_tl) ##This gives me a data.frame

Hi, I have plotted a graph and used a secondary axis as follows: plot (d$Year, d$HrFishing, type='l', col='1', xlab='Year', ylab='Effort (hours/fishing)') par(new=TRUE) plot (d$Year, d$Landings, type='l', col='3', xlab='', ylab='') axis(4) However, if I put a label in the 2nd ylab='' it is placed over the 1st ylab, on the

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

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.

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

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

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

Hello, I have been getting the following intermittent error from kmeans: >str(cavint.p.r) num [1:1967, 1:13] 0.691 0.123 0.388 0.268 0.485 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:1967] "6" "49" "87" "102" ... ..$ : chr [1:13] "HYD" "NEG" "POS" "OXY" ... > set.seed(34) >

Sir, Would like to know what sort of input matrix are taken by the kmeans function defined in mva library of R application. As per the documentation for the Kmeans it takes the following 2 data sets: 1) data 2) centers The commands to be executed in R are as follows: library(mva) data <- read.table('file1',header=TRUE,sep="\t") centers <-

Dear helpers I was working with kmeans from package mva and found some strange situations. When I run several times the kmeans algorithm with the same dataset I get the same partition. I simulated a little example with 6 observations and run kmeans giving the centers and making just one iteration. I expected that the algorithm just allocated the observations to the nearest center but think this

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

Hello, here is a working reproducible example which crashes R using kmeans or gives empty clusters using the nstart option with R 15.2. library(cluster) kmeans(ruspini,4) kmeans(ruspini,4,nstart=2) kmeans(ruspini,4,nstart=4) kmeans(ruspini,4,nstart=10) ?kmeans either we got empty always clusters and or, after some further commands an segfault. regards, Detlef Groth ------------ [R] Empty

I am trying to generate kmean of 10 clusters for a 165 x 165 matrix. i do not see any errors known to me. But I get this error on running the script Error: empty cluster: try a better set of initial centers the commands are M <-matrix(scan("R_mutual",n = 165 * 165),165,165,byrow = T) cl <- kmeans(M,centers=10,20) len = dim(M)[1] .... .... I ran the same script last night and

Hello, I would like to have any help with the function Kmeans of R.. I use this to do a classification of my data...I have chosen 12 classes but, I have always an error message: Error: empty cluster: try a better set of initial centers So, I don't understand the probleme with this function.. Thank you to help me!! All the Best Clothilde Clothilde Kussener CNRS - CEBC 79360 Villiers en bois

Hi Team, I am trying to run kmeans in R, and I need to save the different clusters into different folders. How can I achieve this? # this is how my data looks. $ *cat 1.tsv | head* userid bookid rating bookTotalRatings bookAvgRating userTotalRatings userAvgRating 1 100 0 24 2.7916666666666665 291 2.6735395189003435 2 200 7 24 2.9583333333333335 6 7.0

Dear all. I am searching for KMeans ++ for R. I cannot find it. Do you know any package with it? Best regards, Rui

Dear experts, I am encountering a version-dependent issue. My laptop runs Ubuntu 12.04 LTS 64-bit, R 2.14.1; the issue explained below never occurred with this version of R My desktop runs Ubuntu 11.10 64-bit, R 2.13.2; what follows applies to this setup. The data I'm clustering is constituted by the rows of a 320 x 6 matrix containing integers ranging from 1 to 7, no missing data. I applied

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

Hello all, I've been comparing results from kmeans() in R to PROC FASTCLUS in SAS and I'm getting drastically different results with a real life data set. Even with a simulated data set starting with the same seeds with very well seperated clusters the resulting cluster means are still different. I was hoping to look at the source code of kmeans(), but it's in C and FORTRAN and