similar to: K Means Clustering Weighted by Frequency

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

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:

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, 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

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Hello, I'm new using R. I'm trying to develop a K-means Clustering with R for some data I have, however each time I use that instruction with the same data my cluster means, clustering vector and within cluster sum of square change and I don't understand why because I use the same parameters and the same data. Can anybody explain me why does it happen? Thank you Act. Calef

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

Hi, I need some answers regarding to R. In K-means clustering ,for K=2, How can I find the members of each cluster and What are the coordinates for the cluster centers? Please send me a reply soon. Thank You Sent from Windows Mail [[alternative HTML version deleted]]

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

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

Hi, I have used the kmean fucntion in R to produce some results for my analysis. I like to know the specific underlying algorithm used for the implementation of the function kmean in R. I tried looking for some documents but could not find any. I obtained the kmean result for k ranging from 2 to 10. When i did this initally it worked perfectly. When i tried running again i get the error

Hi, all I would like to use R to perform k-means clustering on my data which included 33 samples measured with ~1000 variables. I have already used kmeans package for this analysis, and showed that there are 4 clusters in my data. However, it's really difficult to plot this cluster in 2-D format since the "huge" number of variables. One possible way is to project the

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]

K-means recluster data with given cluster centers Dear R user, I have several large data sets. Over time additional new data sets will be created. I want to cluster all the data in a similar/ identical way with the k-means algorithm. With the first data set I will find my cluster centers and save the cluster centers to a file [1]. This first data set is huge, it is guarantied that cluster

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, I like to present the results from the clustering method k-means in terms of variances: within and between Cluster. The k-means object gives only the within cluster sum of squares by cluster, so the between variance part is missing,for calculation the following table, which I try to get. Number of | Variance within | Var between | Var total | F-value Cluster k | cluster | cluster

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]]

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, i want a visualization of the k-mean clustering.which one method will be best for visualization?? thnkx [[alternative HTML version deleted]]

I am running a k-means clustering code in R : mydata_kmeans5 <- kmeans(mydata, centers=5).. But the problem is that the data is having some "NA" in it. So R is showing me a message :Error in switch(nmeth, { : NA/NaN/Inf in foreign function call (arg 1) In addition: Warning messages: 1: In switch(nmeth, { : NAs introduced by coercion 2: In switch(nmeth, { : NAs introduced by