similar to: custom metric for dist for use with hclust/kmeans

Hi, I want to know which distance is using in the function kmeans and if we can change this distance. Indeed, in the function pam, we can put a distance matrix in parameter (by the line "pam<-pam(dist(matrixdata),k=7)" ) but we can't do it in the function kmeans, we have to put the matrix of data directly ... Thanks in advance, Nicolas BOUGET

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

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

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.

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

Hi, How to plot the dendrogram or tree for kmeans, like we do for hclust ? [[alternative HTML version deleted]]

kalish at psy.uwa.edu.au wrote: > > I'm a newbie at R, and can't get libraries to really work. > I did this: > > library(help = mva) > cancor Canonical Correlations > cmdscale Classical (Metric) Multidimensional Scaling > dist Distance Matrix Computation > hclust Hierarchical Clustering

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 All, I have a data, suppose it is an N*M matrix data. All I want is to classify it into, let see, 3 classes. Which method(s) do you think is(are) appropriate for this purpose? Any reference will be welcome! Thanks! Best, Baoqiang Cao

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)

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

Gives strange results. I get 'weird' dendrograms with canberra / binary distance metric and median / centroid cluster methods. Is this just my data? Dan

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]]

Hi the devel list, I am using K means with a non standard distance. As far as I see, the function kmeans is able to deal with 4 differents algorithm, but not with a user define distance. In addition, kmeans is not able to deal with missing value whereas there is several solution that k-means can use to deal with them ; one is using a distance that takes the missing value in account, like a

Hello, I have trouble with a data set that comprises 2136 lines of 20 columns. I would like to do a hierarchical clustering and I tried the following: ages.hclust <- hclust(dist(ages, method="euclidean"), "ward") but I get the following error message: Error: cannot allocate vector of size 17797 Kb When I try to do the dist() alone first without the hclust(), I get the

Hello, I've been working on the KMeans clustering algorithm recently and since the past week, I have been stuck on a problem which I'm not able to find a solution to. Since we are representing documents as Tf-idf vectors, they are really sparse vectors (a usual corpus can have around 5000 terms). So it gets really difficult to represent these sparse vectors in a way that would be

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

Hi all, I have a matrix of about 100.000?x 4?that I need?to classify using euclidean metric. For that I am using dist?or daisy functions, but I am afraid that the message: Error in vector("double", length) : vector size specified is too large, means too much lines. Can anyone suggest me how should I analyse this matrix? Thanks in advance, Diogo Andr? Alagador MNCN,CSIC, Madrid, Spain

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