As Ismail notes, you did not give us your code, only a few disconnected bits of your code. Assuming that by "top 1 group" you mean the largest group, here is a reproducible example:
# First create a reproducible set of data set.seed(42) mydata <- matrix(rnorm(300, 50, 10), 100, 3) # A matrix with 100 rows and 3 columns of random normal variates # Run kmeans and look at the structure of the returned object mydata.km <- kmeans(mydata, centers=10) str(mydata.km) List of 9 $ cluster : int [1:100] 5 9 3 6 1 1 10 1 10 8 ... $ centers : num [1:10, 1:3] 53.8 31.8 54.5 40.1 61 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:10] "1" "2" "3" "4" ... .. ..$ : NULL $ totss : num 29069 $ withinss : num [1:10] 601 868 443 1242 717 ... $ tot.withinss: num 6554 $ betweenss : num 22515 $ size : int [1:10] 13 10 9 11 10 5 7 14 13 8 $ iter : int 3 $ ifault : int 0 - attr(*, "class")= chr "kmeans" # "size" is the number of observation in each cluster # "cluster" is the cluster membership for each observation which.max(mydata.km$size) [1] 8 table(mydata.km$cluster) 1 2 3 4 5 6 7 8 9 10 13 10 9 11 10 5 7 14 13 8 # which.max() shows you which cluster is the # largest, cluster number 8 # By sorting "size" you lost the information # about which cluster was the largest # table() shows you the number of observations in each cluster # You can see that cluster 8 has 14 observations # Now print the 14 observations that belong to cluster 8 mydata[mydata.km$cluster == 8, ] [,1] [,2] [,3] [1,] 49.37286 51.19161 48.14622 [2,] 47.21211 44.95783 49.15892 [3,] 56.35950 46.17666 50.37415 [4,] 47.15747 44.87350 48.67912 [5,] 48.28083 51.24702 44.78204 [6,] 45.69531 45.71741 48.25982 [7,] 47.42731 43.86328 55.15668 [8,] 54.55450 55.67621 47.28236 [9,] 56.42899 47.26354 51.90019 [10,] 50.89833 41.99718 50.46564 [11,] 55.81824 51.63207 53.83847 [12,] 50.88440 53.68807 44.30694 [13,] 48.79103 52.94654 56.35514 [14,] 45.23826 46.54912 54.46041 ------------------------------------- David L Carlson Department of Anthropology Texas A&M University College Station, TX 77840-4352 -----Original Message----- From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Ismail SEZEN Sent: Sunday, May 21, 2017 10:09 PM To: θ ” <yarmi1...@hotmail.com> Cc: r-help@r-project.org Subject: Re: [R] How to extract text contexts after clustering. 1- PLEASE do read the posting guide http://www.R-project.org/posting-guide.html 2- PLEASE, first _read_ help for kmeans (?kmeans) function before using function. > On 22 May 2017, at 05:33, θ ” <yarmi1...@hotmail.com> wrote: > > hi: > I need to extract the text contexts of top 1 group after clustering. > But I have no idea how to sort the cluster size then extract the contexts of > top 1 clusters. There isn’t a _top_ cluster for kmeans algorithm. There are _only_ clusters! > > here is my cluster code: > >> file <- read.csv("SiC CMP.csv", header = TRUE) We don’t know what is in file$Main.IPC. >> cluster_k<-length(unique(file$Main.IPC)) >> cl <- kmeans(IPC_Dtm , cluster_k) What is IPC_Dtm? > > > I have tried use�� > >> sort(cl$size, decreasing=T) if you read the documentation, you would know cl$size means the number of points in each cluster. So, why do you sort them? > [1] 341 107 104 80 51 22 15 11 10 8 8 5 5 5 4 4 4 3 > 3 2 2 > [22] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 > 1 1 1 > [43] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 > > But I have no idea how to extract the contexts of top 1 cluster. If you read the _Value_ section of kmeans documentation, you will have an idea how to extract context by using cl$cluster. > > > Eva > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.