similar to: choosing the correct number of clusters using NbClust

I am attempting to use NbClust to examine different indices to determine the number of clusters. The code appears to be working and I am getting results. However, I have noticed that each time I run it I may get different answers. For example looking at the DB index one run recommended 6 clusters while the next 5 - the code is exactly the same. I assume there is some randomization that I

Dear All,i applied "NbClust", to my data to find optimum number of clusters, and got following resultsNow, i don't know how to read these results. more precisely, i would like to know, how to see which method is more precise for my data considering these index values.your help is needed...thanks in advance Eliza Botto > dput(Eliza)structure(list(All.index = structure(c(2, 3, 4, 5,

i m using a package NbClust for cluster analysis. in the following algorithm ->NbClust(m, diss="NULL", distance = "euclidean", min.nc=2, max.nc=15, method = "ward", index = "all", alphaBeale = 0.1) i want to define my own dissimilarity matrix of dimension 38*38. my original data "m" is a matrix of 365*38. whenever i define my own dissimilarity

Hi, I already have a dissimilarity matrix and I am submitting the results to the elbow.obj method to get an optimal number of clusters. Am I reading the below output correctly that I should have 17 clusters? code: top150 <- sampleset[1:150,] {cluster1 <- daisy(top150 , metric = c("gower") , stand = TRUE , type = list(symm

Buenos dias, desde hace algunos dias estoy realizando un trabajo,mi computadora es una DELL, windows 10 64 bits, 8G de RAM y disco de estado solido, estoy procesando 29000 filas y 23 columnas, mi codigo es este: nb <- NbClust(datos.scaled, distance = "euclidean", min.nc = 2, #max.nc = 10, method = "complete", index ="all") . y mi error es este: Error:

Dear Christian, first of all thank you for your answer. I am going to parse through the pages you told me. Meanwhile I'd like to note that probably it is a good idea to put 2-3 lines of R-code demonstrating such a simple needs somnewhere in docs of `cluster' package. E.g. x<-rnorm(500) ... # output means we have rather 1 claster x<-c(rnorm(500), rnorm(500)+5)

SAS has something called the "cubic criterion" cutoff for finding the most appropriate number of clusters. Does R have anything that would replicate that? I've been searching the lists and can't seem to find anything that would point me in the right direction. Thank in advance, Philip Bermingham

Hello, I'm playing around with cluster analysis, and am looking for methods to select the number of clusters. I am aware of methods based on a 'pseudo F' or a 'pseudo T^2'. Are there packages in R that will generate these statistics, and/or other statistics to aid in cluster number selection? Thanks, John. --

Hi all, once more about the old subj :-) My data has too much various distribution families and for every particular experiment I need just to decide whether the data is "quite homogeneous" or it has two or more clusters. I've revisited the following libraries: amap, clust, cclust, mclust, multiv, normix, survey. And I didn't find any ready-to-use general

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.

Dear R user, I am a biochemist/bioinformatician, at the moment working on protein clusterings by conformation similarity. I only started seriously working with R about a couple of months ago. I have been able so far to read my way through tutorials and set-up my hierarchical clusterings. My problem is that I cannot find a way to obtain information on the rooting of specific nodes, i.e. of

I have a pretty big similarity matrix (2870x2870). I will produce even bigger ones soon. I am using PAM to generate clusters. The desired number of output clusters is a PAM input parameter. I do not know a-priopri what is the best clusters layout . I resorted to the silhouette test. It takes forever as I have to run PAM with all possible numbers of clusters. I wonder whether there is some faster

Hi everyone I have a question about clustering. I've managed using CLARA to get a clustering analysis of a large data set. But now I want to find which is the right number of clusters. The clara.object gives some information like the ratio between maximal and minimal dissimilarity that says (maybe if lower than 1??) if a cluster is well-separated from the other. I've also read something

Dear All, I am using Silhouette to estimate the number of clusters in a microarray dataset. Initially, I used the iris data to test my piece of code as follows: library(cluster) data(iris) mydata<-iris[,1:4] maxk<-15 # at most 15 clusters myindex<-rep(0,maxk) # hold the si values for each k clusters mdist<-1-cor(t(mydata)) #dissimlarity

> > It would be nice not only for me. > > I agree totally. If you belong to R-contributors group then thanks a lot in advance! > The problem is that you have to formalize what a cluster is, > and this is not a well defined notion. > It has different meanings in different applications. you are right if one follows the idea of full formalization of the notion it

Hello, Could someone please let me know the procedure for determining the 'best' solution with regards to the number of clusters using the Fanny algorithm for computing fuzzy clusters? The function requires a specification of the number of clusters a priori, but I am interested in determining what number of clusters would result in the ideal fit with the data. Any help/advice/pointers to

Dear R users, I am interested in clustering in R. In SAS we have some criteria for determining the number of clusters using the PROC CLUSTER procedure, which are "CCC" cubic clustering criterion (Sarl 1981), Psuedo F (PSF), and Psuedo T square (PST). My question is do thsese criterion exists in R, I tried to search and got one hit (BIC) in Mclust, which I am aware of, any input is

Hi: I'm clustering a microarray dataset with a large # of samples. I would like your opinion on the best way to automatically determine the optimal # of clusters. Currently I am using the "cluster" package, clustering with "clara", examining the average silhouette width at various numbers of clusters. I'd like opinions on whether any newer packages offer

Hi, I have produced a dendrogram of categorical data in R using the hclust function, although the input was a dissimilarity matrix produced in SAS, as I have defined my own distances. The dendrogram is fine and I can view and use this. However, I was wondering if there is a method by which I can find out the optimal place to place groups, rather than relying on my visual analysis? I don't

Hi: I'm clustering a microarray dataset with a large # of samples. I would like your opinion on the best way to automatically determine the optimal # of clusters. Currently I am using the "cluster" package, clustering with "clara", examining the average silhouette width at various numbers of clusters. I'd like opinions on whether any newer packages offer