similar to: cclust causes R to crash when using manhattan kmeans

I have checked that section already. Sorry, I should have mentioned that. Memory limit increase does not work. Installtion of msvcrt.dll does not work either. Thank you. -----Original Message----- From: ripley at stats.ox.ac.uk [mailto:ripley at stats.ox.ac.uk] Sent: Wednesday, March 05, 2003 2:44 PM To: Igor Oleinik Cc: r-help at stat.math.ethz.ch Subject: Re: [R] problem with cclust[er]

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.

Hello, I am calling cclust function in cclust package repeatedly until some ceratain conditions for a cluster are met. Unfortunately, the system crashes on the second call (after debugging). # kmeans res1 is a well defined matrix cl <- cclust(res1, as.numeric(ncntrs), iter.max = 20, verbose = FALSE, dist="manhattan", method="kmeans") RGui has generated errors and will

I would like to see an example of a data matrix for cclust and how to import it to cclust. In fact, i don't know how to give my imput for cclust program! i test this file 1 0.23 1.52 2 0.52 1.25 3 0.13 1.89 4 0.78 1.11 i do >library(cclust) >x<-scan("test.matrice.phyl") >cclust(x,2,method="kmeans") i have this error message: Error in sample(length(x),

To Whom It May Concern, I am currently trying to use R to perform a "kmeans" clustering of a three dimensional data set. In the directory R-1.5.1/library/cclust/data/ I have created a file that has the following format (only the first few lines are shown for brevity): B X.Vtl X.Vtu 1 -0.529043 1.307031 1.625169 2 -0.752502 1.132813 1.480548 3

Hello, here is my problem, After looking at the mail archives, I found a description of the error I get when I use this package. At first I even tought that they were showing how to solve it. But the thing is that by saying "the programmer forgot drop=FALSE" doesn't show me how I should get rid of the problem I have looked inside the package very quickly and I found three

Hi all, once more about the old subj :-) My data has too much various distribution families and for every particular experiment I need just to decide whether the data is "quite homogeneous" or it has two or more clusters. I've revisited the following libraries: amap, clust, cclust, mclust, multiv, normix, survey. And I didn't find any ready-to-use general

Full_Name: Chris Hane Version: 2.10.1 OS: Windows Submission from: (NULL) (198.203.181.181) When using barchart in the flexcust package, setting mcol=NULL to avoid the lollipops causes an error. Each panel shows the text message "Error using packet n replacement has length zero." where n is the panel number. > data(iris) > cl <- cclust(iris[,-5], k=3) > barplot(cl,

Hi, I am using the following syntax to enter data and perform a cluster analysis: x <- read.table ("clstrdbt.csv", header=TRUE, sep = ",",fill = TRUE) cl<-cclust(x,4,20,verbose=TRUE,method="kmeans") This is the result I receive: Error in cclust(x, 4, 20, verbose = TRUE, method = "kmeans") : (list) object cannot be coerced to type 'double'

Hello all. I wish to run k-means with "manhattan" distance. Since this is not supported by the function "kmeans", I turned to the "pam" function in the "fpc" package. Yet, when I tried to have the algorithm run with different starting points, I found that pam ignores and keep on starting the algorithm from the same starting-points (medoids). For my

Hi, I'm trying to debug a function I wrote to calculate the cindex for a hierarchical tree. For this it is useful to compare my calculations with those in output from the clustindex function, in the cclust library. There's no way, however, to have the cindex value for a given output of the cclust function, as a NA value is always returned. This happens almost surely because the cindex in

Dear R-Devel subscriber, as a follow up to my yesterday's email: I tested an analogous example with the S4-package "flexclust" by executing the following code: library(flexclust) example(cclust) cl After saving the work space and starting a new R process with the restored work space, the same behaviour (i.e., the methods pertinent to "flexclust" are not used, even after

I am trying to cluster some binary data using k-means . As the regular "kmeans" available from stats package in R does'nt provide the option to change the distance method. I was wondering there is any package available to specify type of distance measure to be used in k means clustering in R. Especially distances like "Jaccard" which is good for binary data.

Sorry, to bother the list one more time: but the following worked at least for 'urca': in NAMESPACE I now included explicitly: import(methods) a fix of the 'urca'-package will be uploaded to CRAN on the weekend. Fritz, will this work for ypur package 'flexclust' too? I have in my DESCRIPTION imports: methods and in flexclust it is in depends: methods. However, both

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

There is a new version of the 'CCLUST' package ,where i removed the extra command for the kmeans algorithm in the .R programm and also the comments about it in the .Rd help page. Now in the cclust library the kmeans algorithm can be applied only by using the cclust function. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

There is a new version of the 'CCLUST' package ,where i removed the extra command for the kmeans algorithm in the .R programm and also the comments about it in the .Rd help page. Now in the cclust library the kmeans algorithm can be applied only by using the cclust function. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi all, I have a dataset with numeric and factor columns of data which I developed a Gower Dissimilarity Matrix for (Daisy) and used Agglomerative Nesting (Agnes) to develop 20 clusters. I would like to use the 20 clusters to determine cluster membership for a new dataset (using predict) but cannot find a way to do this (no way to "predict" in the cluster package). I know I can use

I have to solve a clustering problem. My first step is to determinate the number of clusters, that's why I 'm using the Calinski index ( [tr(b)/(k-1)]/[tr(w)/(k-1)] ) which i try to maximize to have the best number of clusters. A function is already implemented in R to calculate this index : clustIndex(cl,x, index="calinski") where cl is the result of a clustering method ,

Hi all, I am interested in performing a cluster analysis on ecological data from forests in Pennsylvania. I would like to develop definitions for forest types (red maple forests, upland oak forests, etc.(AH AR in attached table)) based on measured attributes in each forest type. To do this, I would like to 'draw clusters' around forest types based on information from various tree