search for: fanny

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...

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)), cbind(rnorm(15, 5, 0.5), rnorm(15, 5, 0.5)), cbind(rnorm( 3,3.2,0.5), rnorm( 3,3.2,0.5))) .proctime00 <- proc.time() (fannyx <- fanny(x, 2)) and got th...

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( 3,3.2,0.5), rnorm( 3,3.2,0.5))) .proctime00 <- proc.time() (fannyx <- fanny(x, 2)) and got the following messages: E...

Hi all, let's say I have matrix People Desc Value Mary Height 50 Mary Weight 100 Fanny Height 60 Fanny Height 200 Is there a quick way to form the following matrix? People Height Weight Mary 50 100 Fanny 60 200 (Assuming I don't know the length of people/desc and let's say these are characters matrix.. I tried play with row(), co...

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 s...

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". Look...

https://bugs.freedesktop.org/show_bug.cgi?id=70388 Priority: medium Bug ID: 70388 Assignee: nouveau at lists.freedesktop.org Summary: [NV34] failed to idle channel 0xcccc0000 Severity: critical Classification: Unclassified OS: Linux (All) Reporter: rosti.bsd at gmail.com Hardware: x86 (IA32)

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 function everything works fine... What...

Hello, Instead of receiving only one message when somebody send a question or a answer, I receive 2 messages with exactly the same contents inside. Is it the same for you? Thank you, Fanny -------------------------- Fanny AZIZA École Nationale Vétérinaire d'Alfort Mail: faziza@vet-alfort.fr Tél. 01 43 96 70 33 [[alternate HTML version deleted]]

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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 resources on the WWW would be greatly appreciated. Th...

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 the game is rigged. Don't let that stop yo...

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 cacmnet.co...

...e : > foo [1] 68 NA 33 63 53 62 44 NA 20 69 NA 62 59 43 51 19 38 57 30 53 62 67 42 31 38 [26] 50 NA 69 67 38 NA 26 NA 52 39 45 42 58 79 92 53 NA 22 21 30 38 64 49 43 28 [51] 33 42 59 32 41 52 44 54 37 43 32 42 59 39 74 38 33 56 NA 52 38 46 42 29 58 [76] 54 62 32 53 39 28 34 24 44 46 27 38 > fanny(foo, k=2, diss=FALSE) Error in fanny(foo, k = 2, diss = FALSE) : No clustering performed, NA-values in the dissimilarity matrix. The help page says : In case of a matrix or dataframe, each row corresponds to an observation, and each column corresponds to a variable. All v...

...of (random) data. Thank you in advance. Hans __________________________________ />>>>> On Tue, 09 Jan 2001 15:42:30 -0700, / />>>>> Jose Quesada (JQ) wrote: / / > Hi, / / > I'm trying to use a similarity matrix (triangular) as input for pam() or / / > fanny() clustering algorithms. / / > The problem is that this algorithms can only accept a dissimilarity / / > matrix, normally generated by daisy(). / / > However, daisy only accept 'data matrix or dataframe. Dissimilarities / / > will be computed between the rows of x'. / / > Is...

...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 see which of them can get same components as the return value of "kmeans", by using the following codes: >x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2), matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2)) >cl <- km...

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 memberships for all cluster. One solution suggested to the p...

...ed 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 -c twins.f -o twins.o gcc -shared -o cluster.so clara.o daisy.o dysta.o fanny.o meet.o mona.o pam.o spannel.o twins.o -L/usr/lib/gcc-lib/i486-linux/3.3.4 -L/usr/lib/gcc-lib/i486-linux/3.3.4/../../.. -lfrtbegin -lg2c -lm -lgcc_s -L/usr/lib/R/lib -lR make[1]: Leaving directory `/tmp/buildd/cluster-1.9.6/src' ** R ** data ** moving datasets to lazyload DB ** preparing pac...

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...

Dear R-help list members i would like to use R to produce clustering or partitioning of a dataset. I am trying to use the functions: - hierclust() of the package multiv -pam(), agnes() and fanny() of the package cluster But I cannot get any result because of lack of memory. Would you know any clustering function not to greedy in memory? I have tried to expand my memory limit with memory.limit() but no significant results. Thank you for any answer that could help. Vincent