similar to: K-means clustering with NA

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 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

Hi. I am trying to modify kmeans function. It seems that is failing something obvious with the workspace. I am a newbie and here is my code: myk = function (x, centers, iter.max = 10, nstart = 1, algorithm = c("Hartigan-Wong", + "Lloyd", "Forgy", "MacQueen")) + { + do_one <- function(nmeth) { + Z <- switch(nmeth, { + Z

I am importing a dataset in R where some of the variable are numerical and some of them are character...but the problem is that R is treating numerical variables as character (I am using "is.character" to judge the type). Now the question is how can I convert these character variables into numeric and also let me know about the other conversion like "numeric to

Kindly let me know the process to carry out a Linear discriminant analysis...thanks in advance Arup -- View this message in context: http://www.nabble.com/Linear-Discriminant-Analysis-tp22199424p22199424.html Sent from the R help mailing list archive at Nabble.com.

I can't import any HTML or SQL files into R..:confused: Please suggest me the packages for these two file types and also let me know the syntax for importing these two type of files. Thank you in advance. Arup -- View this message in context: http://www.nabble.com/How-to-import-HTML-and-SQL-files-tp21830945p21830945.html Sent from the R help mailing list archive at Nabble.com.

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

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

Hello all, A solaris server named bellow solarisserver share via samba (Samba 3.0.13) with network. Bellow an extract from the smb.conf file. [global] workgroup = WORKGROUP server string = solarisserver security = SERVER password server = passwordserver username map = /C/netlocal/samba/lib/users.map log level = 1 log file =

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:

Dear sir; may you drop me some idea how can i get rid of following error message: Error in switch(nmeth, { : NA/NaN/Inf in foreign function call (arg 1) i dont know what does nmeth and ther rest of error message mean? i have a file which contains 460 rows and 174 columns including missing value as NA. Regards, Mostafa

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

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

I'm looking for a scheme to generate a default color palette for plotting points, lines and text (on a white or transparent background) with from 2 to say 9 colors with the following constraints: - "red" is reserved for another purpose - colors should be highly distinct - avoid light colors (like "yellow"s) In RColorBrewer, most of the schemes are designed for area fill

Dear R users I have the matrix of the centres of some clusters, e.g. 20 clusters each with 100 dimentions, so this matrix contains 20 rows * 100 columns numeric values. I have collected new data (each with 100 numeric values) and would like to keep the above 20 centres fixed/'unmoved' whilst just see how my new data fit in this grouping system, e.g. if the data is close to cluster 1

*Apologies if this is not the right way to ask a question, I'm a first timer posting here. Does anyone have a solution to this? I'm having trouble figuring out how to use weighting with K Means Clustering. So say if my dataset is: Column 1 = x coords Column 2 = y coords Column 3 = frequency each coordinate occurs So I'm basically trying to weight the points more heavily if

Dear members. I am having problems to understand the kmeans- results in R. I am applying kmeans-algorithms to my big data file, and it is producing the results of the clusters. Q1) Does anybody knows how to find out in which cluster (I have fixed numberofclusters = 5 ) which data have been used? COMMAND (kmeans.results <- kmeans(mydata,centers =5, iter.max= 1000, nstart =10000)) Q2) When I

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

Hi all, I am trying to run the k-means cluster analysis using the function kmeans in the package cluster. The data are: x = c(-0.26, -0.23, -0.05, -0.20, 0.30, -0.84, -0.10, -0.12, 0.10, -0.31, -0.19, 0.18, -0.26, -0.23, -0.37, -0.23) I've got two different solutions when I ran this function over a few times: kmeans(x, centers=2) The first solution gives the following: $cluster [1]

Hello, I have two matrices as shown below: Matrix 1 ID AB1 BC1 CD1 ... name1 1,1 2,1 0,2 ... name2 2,0 1,2 1,2 ... name3 0,2 1,1 2,0 ... name4 2,0 0,2 0,2 ... Matrix 2 ID AB2 BC2 CD2 ... name1 1,1