similar to: about arguments in "bclust"

Hi All, I run function "kmeans" to cluster a matrix. But when the matrix size is big enough, it keeps saying "did not converge in 10 iterations". Could you explain what it means and if the result is wrong? And the interesting thing is sometimes this warning happens when the sample size is around 51200 x 6, sometimes it happens around 30000 x 6. Does the warning related

Hi All, Could you please help with the error I get from the following codes? > Library(cluster) > data(iris) > bc1 <- bclust(iris[,2:5], 3, base.method="clara", base.centers=5) Then I got: "Committee Member: 1(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) Error in bclust(iris[, 2:5], 3, base.method = "clara",

Hi All, For the function "bclust"(e1071), the argument "base.method" is explained as "must be the name of a partitioning cluster function returning a list with the same components as the return value of 'kmeans'. In my understanding, there are three partitioning cluster functions in R, which are "clara, pam, fanny". Then I check each of them to

Hi, I'm having some difficulties formulating this question. But what I want, is to extract the options associated with a parameter for a function. e.g. method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN") in the optim function. So I would like to have a vector with c("Nelder-Mead", "BFGS", "CG",

Hi, I am new to clustering and was wondering why pvclust using "maximum" as distance measure nearly always results in p-values above 95%. I wrote an example programme which demonstrates this effect. I uploaded a PDF showing the results Here is the code which produces the PDF file: ------------------------------------------------------------------------------------- s <-

Hi everyone, Currently i'm trying to understand the bagged clustering algorithm, bclust {e1071}. When I run the given example in the help file (as below) data(iris) bc1 <- bclust(iris[,1:4], 3, base.centers=5) plot(bc1) and plot the bclust object, 2 graphs are produced. The first is a dendrogram, but what is the second plot? The axes are not labelled and what do the two

Hello everybody, I have a problem with the commando of boxplot -bclust. http://127.0.0.1:13155/library/e1071/html/boxplot.bclust.html > data(iris) > bc1 <- bclust(iris[,1:4], 3, base.centers=5) Committee Member: 1(1) 2(1) 3(1) 4(1) 5(1) 6(1) 7(1) 8(1) 9(1) 10(1) Computing Hierarchical Clustering > boxplot(bc1) Warnmeldungen: 1: In if (x$datamean) { : Bedingung hat Länge > 1 und

the first line of dist() says if (!is.na(pmatch(method, "euclidian"))) shouldn't that be "euclidean" ? --------------------- R version 2.5.1 (2007-06-27) i486-pc-linux-gnu locale:

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 a very large dataset of 3008 individuals and 800 numerical variables. In fact it is a table of 3008 36-monthes multivariated time series that I would like to classify with an unsupervised algorithm I had a look at the function kkmeans of e1071 package, which seems to be a kernel weighted version of the algorithm algorithm, and the bclust from the same package which does bootstrapping

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

I'm working on a project related to document clustering. I know that R has clustering algorithms such as clara, but only supports two distance metrics: euclidian and manhattan, which are not very useful for clustering documents. I was wondering how easy it would be to extend the clustering package in R to support other distance metrics, such as cosine distance, or if there was an API for

Dear All: I'm a newbie to R and chemometrics. Now I'm trying apply bclust on fuzzy c-means like this: >bc1 <- bclust(iris[,1:4], 3, base.centers=20,iter.base=100, base.method="cmeans") Committee Member: 1(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)Erro r in bclust(iris[, 1:4], 3, base.centers = 20, iter.base = 100, base.method =

Hi guys, I've been using the kmeans and hclust functions for some time now and was wondering if I could specify a custom metric when passing my data frame into hclust as a distance matrix. Actually, kmeans doesn't even take a distance matrix; it takes the data frame directly. I was wondering if there's a way or if there's a package that lets you create distance matrices from

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 everyone, I have the following question: given three objects let's say: a <- c( 2 , 5, 15, 16) b <- c(1 ,1, 8 , 8) c <- c (10, 10 11 ,11) m<-matrix(c(a,b,c),byrow=T,nrow=3) rownames(m)<-c("gene a", 'gene b','gene c') m gene.dist<-dist(m,method='euclidian') gene.dist which is the best way to plot their evolution in time? shoul I use a

I have a data set with 6 variables and 251 cases. The people who supplied me with this data set believe that it falls naturally into three groups, and have given me a rule for determining group number from these 6 variables. If I do scaled.stuff <- scale(stuff, TRUE, c(...the design ranges...)) stuff.dist <- dist(scaled.stuff) stuff.hc <- hclust(stuff.dist)

Dear all, I have a problem with the function "bagged clustering" of package e1071. When I try to run the example of bagged clustering with the iris data : data(iris) bc1 <- bclust(iris[,1:4], 3, base.centers=5) I got the following message error : Loading required package: class Committee Member: 1(1) 2(1) 3(1) 4(1) 5(1) 6(1) 7(1) 8(1) 9(1) 10(1)Error in bclust(iris[, 1:4], 3,

Dear All, Is there any existing code for plotting silhouette for kmeans clustering results? Many thanks! Linda [[alternative HTML version deleted]]