similar to: weithed clustering (was: Re: problems with a large data set)

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 imported a csv, semi-colon spearated file with read.csv2, containing one column of rownames and one column of floating point numbers. When I look at the column of data with framename$columnname, I get the values of the column plus level values. Are these level values created automatically ? The problem is when I try to calcluate the correlation coefficient between this set of data

And route print? Do you have any servers on the WAN? (or is everyone connecting to the servers THROUGH the WAN?) Can you see the samba machines at all in network neghborhood? What happens when you type in the UNC of a samba server manually? (i.e. \\SAMBASERVERNAME) I hope I can help you, and I'm trying, but I'm not a samba guru. -Dan PS. Please change your email type to PLAIN TEXT from

Hi there, I use function "kmeans" and "clara" to cluster one flow cytometry dataset. By using function "plot", the clusters got from "clara" can be graphed, while "kmeans" not. How can I get the plot of the clusters of "kmeans"? And, I hope to compare the two methods "kmeans" and "clara", or in other word, I

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

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

Hello R users It's some time I am playing with a dataset to do some cluster analysis. The data set consists of 14 columns being geographical coordinates and monthly temperatures in annual files latitutde - longitude - temperature 1 -..... - temperature 12 I have some missing values in some cases, maybe there are 8 monthly valid values at some points with four non valid. I don't want to

Peter, As I understand your Q. You probably have data that is similar to each other like stock Prices for all RHS variable. In that case the difference between corr and cov is not significant; however, if your RHS contains totally dissimilar variables it matters a great deal. If x1 income, x2 job type, x3 Education level, etc..., then taking cov of these variables would not be desireable

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 use kmeans to classify spectral events in high and low 1/3 octave bands: #Do cluster analysis CyclA<-data.frame(LlowA,LhghA) CntrA<-matrix(c(0.9,0.8,0.8,0.75,0.65,0.65), nrow = 3, ncol=2, byrow=TRUE) ClstA<-kmeans(CyclA,centers=CntrA,nstart=50,algorithm="MacQueen") This works well when the actual data shows 1,2 or 3 groups that are not "too close" in a cross plot.

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

Dear WizaRds, My goal is to program the VS-KM algorithm by Brusco and Cradit 01 and I have come to a complete stop in my efforts. Maybe anybody is willing to follow my thoughts and offer some help. In a first step, I want to use a single variable for the partitioning process. As the center-matrix I use the objects that belong to the cluster I found with the hierarchial Ward algorithm. Then,

Dear *, I have to perform an ANOVA analysis to study the effects on the performance of some algorithmic components in the design of my algorithm. I have done a full factorial design for the following 5 factors (my response variable is "best"): $ model :'data.frame': 3360 obs. of 6 variables: ..$ best : num [1:3360] 0.108 0.573 0.625 1.057 0.451 ...

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, I have been trying for a few days to do kmeans on a matrix of data which I populate from java. Here's my code: String[] Rargs = {"--vanilla"}; Rengine re = new Rengine(Rargs, false, null); System.out.println("Rengine created, waiting for R"); // the engine creates R is a new thread, so we should wait until it's // ready if

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:

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

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

Hello, I am trying to create a heatmap that clusters based on a k-means scheme rather than a hierarchical clustering scheme. Suppose I have the following input data, located in sample.table: x1 x2 x3 x4 x1 17.198 16.306 16.806 16.374 x2 14.554 10.866 15.780 14.596 x3 14.374 14.118 14.569 17.352 x4 17.505 14.596 15.738 14.070 By using the heatmap.2 function as follows, I can create a heatmap

Dear list, I am trying to use the SSOAP package to access a very simple SOAP service, included as a demonstrator in the axis2 Apache package: "Version" (takes no input parameters and should return a string with the version number). The web service itself seems to be working as I could access it using an other client (eclipse Web Server tester). You will find the service wsdl here: