similar to: Ideal number of clusters using the Fanny algorithm

Dear All, I am attempting to use the FANNY fuzzy clustering function in R (Kaufman & Rousseeuw, 1990), found in the "cluster" package. I have run into a variety of difficulties; the two most crucial difficulties are enumerated below. 1. Where is the 'm' parameter in FANNY? In _Finding Groups in Data: An Introduction to Cluster Analysis_ (1990) by Kaufman & Rousseeuw,

Hi, I am using fanny and I have estrange results. I am wondering if someone out there can help me understand why this happens. First of all in most of my tries, it gives me a result in which each object has equal membership in all clusters. I have read that that means "the clustering is entirely fuzzy". Looking at the graphics it is really difficult to understand how objects with so

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'm trying to obtain a fuzzy clustering with fanny from the cluster package, using a given set of data. That worked just fine. I have another separate sample of data from the same problem. For each case in this new sample I would like to know their membership coefficients with respect to the clustering obtained with the first dataset. In effect I want to have a kind of prediction

Dear All, My R version: 2.0.1 ; OS using: Linux. I install the package cluster by using install.packages("cluster"). After install it, it runs fine for function clara, but it does not work for function fanny. I did the following things: library(cluster) set.seed(21) x <- rbind(cbind(rnorm(10, 0, 0.5), rnorm(10, 0, 0.5)),cbind(rnorm(15, 5, 0.5), rnorm(15, 5, 0.5)),cbind(rnorm(

Full_Name: Guan-Hua Huang Version: 2.0.1 OS: Linux Submission from: (NULL) (140.113.114.123) I install the package cluster by using install.packages("cluster"). After install it, it runs fine for function clara, but it does not work for function fanny. I did the following things: library(cluster) set.seed(21) x <- rbind(cbind(rnorm(10, 0, 0.5), rnorm(10, 0, 0.5)),

Hello, I want to make a fuzzy classification from a dissimilarity matrix (calculated with daisy from package 'cluster'). I have tried to use fanny (package cluster) but I have the same problems than described in a previous message (http://tolstoy.newcastle.edu.au/R/help/05/05/4546.html) i.e. it always gives me two clusters in the results (even if k is different from 2) with the same

I have "small" problem ... with the cluster library each time I try to use the "agnes","pam","fanny" functions with more than 20000 elements I get the following error: >Error in vector("double", length) : negative length vectors are not allowed >In addition: Warning message: >NAs introduced by coercion But with the clara

I've done a little digging, but I haven't been able to find an MPI or snow (or any other distributed processing) implementation of fanny (from cluster) or cmeans. Is anyone aware of some implementations or addons which I've missed? [I know of doi://10.1007/978-3-540-71351-7; but that's not for R] Don Armstrong [Currently not subscribed; M-F-T: set accordingly.] -- Certainly

I think the "cmeans" method in library(e1071) works better for large data sets as "fanny". (note,not for this data: fanny has also problems with standardized large data sets - here produce fanny the same memberships for all observations; cmeans works "correctly") Matthias > -----Urspr??ngliche Nachricht----- > Von: Bhaskar S. Manda [mailto:bhaskar at

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.

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 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

Hello, i use i.e. the cluster-package and the fanny-object to construct some cluster's and get the membership'S for every "Person" & Cluster ! My finally problem is to match efficient the data and the results's togehter in one table! Until now i use the ODBC-Package and save both in a access-file and copy them together, but i can't be sure that the sorting is the

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Hi, I am an engineering student in IIT Guwahati, India. Currently, as a summer project I am working on 'packet loss concealment in voip scenario'. I am eager to know what PLC methods are used at receiver end of speex decoder. And if I could develop some more efficient method I could able to contribute to the speex. Actually, I searched the whole speex manual but failed to find what

Dear All, I hope I am not asking a FAQ. I am dealing with a problem of graph theory [connected components in a non-directed graph] and I do not want to rediscover the wheel. I saw a large number of R packages dealing for instance with the k-means method or hierarchical clustering for spatially distributed data and I am basically facing a similar problem. I am given a set of data which are the

HI, (1) ...thanks for the last comments to tcltk & Win2000. (2) - i'm newbie in tcl/tk but would like learn fast and develop (learning by doing) a tcl/tk clusterAnalysis application which combine some clusterMethods from different Rpackages. i use the tkttest.R example as "reference" and become not finished for my "first" step???? (3) how is it possible that the code

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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