similar to: "partitioning cluster function"

HI R users With clara function I get a data frame (maybe this is not the exact word, I'm new to R) with the following variables: > names(myclara) [1] "sample" "medoids" "i.med" "clustering" "objective" [6] "clusinfo" "diss" "call" "silinfo" "data" I want to

I am using some functions from package clusterSim to evaluate the best clusters layout. Here is the features vector I am using to cluater 12 signals: > alpha.vec [1] 0.8540039 0.8558350 0.8006592 0.8066406 0.8322754 0.8991699 0.8212891 [8] 0.8815918 0.9050293 0.9174194 0.8613281 0.8425293 In the following I pasted an excerpt of my program:

Hi there, I'm doing some clustering analysis and try to find all the algorithms related to clustering in R. Here is the list of the algorithms I found. But I'm not sure if It's the complete list. Could you please check it and see if there're other ones? Thank you very much! P.S.: List of the algorithms related to clustering: (1) Hierarchical methods hclust

Hi All, Just want to make sure, in function "bclust", do the following argument only have one option? argument "dist.method" has one option "Euclidian"; argument "hclust.method" has one option "average"; argument "base.method" has one option "kmeans". Thank you! [[alternative HTML version deleted]]

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",

Dear all, I have a problem when using some classification functions (Kmeans, PAM, FANNY...) with a distance matrix, and i would to understand how it proceeds for the positioning of centroids after one execution step. In fact, in the classical formulation of the algorithm, after each step, to re-position the center, it calculates the distance between any elements of the old cluster and its

After ordering the table of membership degrees , i must get the difference between the first and second coloumns , between the first and second largest membership degree of object i. This for K=2,K=3,....to K.max=6. This difference is multiplyed by the Crisp silhouette index vector (si). Too it dependending on K=2,...,K.max=6; the result divided by the sum of these differences I need a final

Hello all. I wish to run k-means with "manhattan" distance. Since this is not supported by the function "kmeans", I turned to the "pam" function in the "fpc" package. Yet, when I tried to have the algorithm run with different starting points, I found that pam ignores and keep on starting the algorithm from the same starting-points (medoids). For my

Hi there, I notice that some of the clustering methods in R are not appropriate to deal with large data set. Here is the list I make to see which are appropriate or which are not appropriate for large dataset. Could you please take a look and check if it is right or not? I need this information to decide which methods I should choose. Thank you! P.S.: List: Appropriate for large

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 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

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

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

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 R users, When I run the following code, R crashes: require(cclust) x <- matrix(c(0,0,0,1.5,1,-1), ncol=2, byrow=TRUE) cclust(x, centers=x[2:3,], dist="manhattan", method="kmeans") While this works: cclust(x, centers=x[2:3,], dist="euclidean", method="kmeans") I'm posting this here because I am not sure if it is a bug. I've been searching

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 i am using prediction.strength with k-medoids algorithms. There are simple examples like prediction.strength(iriss,2,3,M=3,method="pam") I wrote my code like prediction.strength(data,2,6,M=10,clustermethod=pamkCBI,DIST,krange=2:6,diss=TRUE,usepam=TRUE) because i am using the dissimilarity matrix instead of the data itself for the clustering algorithms. But then i got this error

Doing the normal build process [1] for a first time with a R 2.0.0 snapshot -- the Sep 21 version I uploaded to Debian's 'experimental' section two days ago, ended in failure. The package in question is cluster 1.9.6 which should be 2.0.0-ready. The (partial) log follows: ----------------------------------------------------------------------------- [...] g77 -mieee-fp -fPIC -g -O2