similar to: Dynamic clustering?

Hi All, I have a n x m matrix. The n rows are individuals, the m columns are variables. The matrix is in itself a collection of 1s (if a variable is observed for an individual), and 0s (is there is no observation). Something like: [,1] [,2] [,3] [,4] [,5] [,6] [1,] 1 0 1 1 0 0 [2,] 1 0 1 1 0 0 [3,] 1 0 1 1 0 0 [4,] 0 1 0

Hi, My questions concern the function 'mclustBIC' which compute BIC for a range of clusters of several models on the given data and the other function 'mclustModel' which choose the best model and the best number of cluster accordind to the results of the previous cited function. 1) When trying the following example (see ?mclustModel), I get negative BIC computed by

Dear All, I'm currently doing a project about unsupervised learning, and I'll be using R to analyse a few network traffic datasets downloaded off Andrew Moore's website (http://www.cl.cam.ac.uk/Research/SRG/netos/nprobe/data/papers/sigmetrics/index.html). Could anyone shed some light on how to build a classifier from the training set A, and then use a validation set B to assess the

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

Hello List, My question is an elementary one. I have run a fuzzy kmeans cluster using FANNY to group freshwater fish assemblages. I then went in the field to validate that classification and have retrieved new assemblage data for a new suite of streams. Therefore I would like to use Predict to determine how well the original clustering fits the new data. However I have not figured out a

Hi there I have been using R to perform kmeans on a dataset. The data is fed in using read.table and then a matrix (x) is created i.e: [ mat <- matrix(0, nlevels(DF$V1), nlevels(DF$V2), dimnames = list(levels(DF$V1), levels(DF$V2))) mat[cbind(DF$V1, DF$V2)] <- DF$V3 This matrix is then taken and a distance matrix (y) created using dist() before performing the kmeans clustering. My query

I am using R 1.3.0 on Windows 2000. For an experiment, I am wanting to find the most diverse 400 items to study in a possible 3200 items. Diversity here is based on a few hundred attributes. For this, I would like to do a clustering analysis and find 400 clusters (i.e. different from each other in some way hopefully). From each of these 400 clusters, I will pick a representative. I expect

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 All, I have a data, suppose it is an N*M matrix data. All I want is to classify it into, let see, 3 classes. Which method(s) do you think is(are) appropriate for this purpose? Any reference will be welcome! Thanks! Best, Baoqiang Cao

Hi R friends, I am posting this question even though I know that the nature of it is closer to general stats than R. Please let me know if you are aware of a list for general statistical questions: I am looking for a simple method to distinguish two groups of data in a long vector of numbers: list <- c(1,2,3,2,3,2,3,4,3,2,3,4,3,2,400,340,3,2,4,5,6,4,3,6,4,5,3) I would like to

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

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 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, I just get a question (sorry if it is a dumb one) and I "phase" my question in the following R codes: group1<-rnorm(n=50, mean=0, sd=1) group2<-rnorm(n=20, mean=1, sd=1.5) group3<-c(group1,group2) Now, if I am given a dataset from group3, what method (discriminant analysis, clustering, maybe) is the best to cluster them by using R. The known info includes: 2 clusters,

hi r-help, i am doing kmeans clustering in stats. i tried for five clusters clustering using: kcl1 <- kmeans(as1[,c("contlife","somlife","agglife","sexlife",                         "rellife","hordlife","doutlife","symtlife","washlife",                       

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

Hello, I would like to have any help with the function Kmeans of R.. I use this to do a classification of my data...I have chosen 12 classes but, I have always an error message: Error: empty cluster: try a better set of initial centers So, I don't understand the probleme with this function.. Thank you to help me!! All the Best Clothilde Clothilde Kussener CNRS - CEBC 79360 Villiers en bois

Hi, I have a 1.000 observations with 10 attributes (of different types: numeric, dicotomic, categorical ecc..) and a measure M. I need to cluster these observations in order to assign a new observation (with the same 10 attributes but not the measure) to a cluster. I want to calculate for the new observation a measure as the average of the meausures M of the observations in the cluster

Hello, here is a working reproducible example which crashes R using kmeans or gives empty clusters using the nstart option with R 15.2. library(cluster) kmeans(ruspini,4) kmeans(ruspini,4,nstart=2) kmeans(ruspini,4,nstart=4) kmeans(ruspini,4,nstart=10) ?kmeans either we got empty always clusters and or, after some further commands an segfault. regards, Detlef Groth ------------ [R] Empty

Hi All, These are the code that i used to plot the kmeans clustering. DataSetS01022<-rbind(matrix(rnorm(50),ncol=2), + matrix(rnorm(50),ncol=2)) > colnames(DataSetS01022) <-c("timeslot","em") > (cl <-kmeans(DataSetS01022,2)) >plot (DataSetS01022, col=cl$cluster) > points(cl$centers, col = 1:2, pch = 8, cex=2) Now, i would like to do *the same process