similar to: K-Centroids fuzzy Clusters Analysis ?

Hello, Can someone point me to documentation or ideas on how to calculate the centroids of clusters identified with hclust ? I would like to be able to chose the number of clusters (in the style of cutree) and then get the centroids of these clusters. This seems like a quite obvious task to me, but I haven't been able to put my hands on a relevant command. Thank you, Moritz

Hi the devel list, I am using K means with a non standard distance. As far as I see, the function kmeans is able to deal with 4 differents algorithm, but not with a user define distance. In addition, kmeans is not able to deal with missing value whereas there is several solution that k-means can use to deal with them ; one is using a distance that takes the missing value in account, like a

Hey Parth, Thanks for the reply. I am considering implementing a cosine distance metric too, along with euclidian distance because of the dimensionality issue that comes in with K-Means and euclidian distance metric. That does help when we deal with sparse vectors for documents. The particular problem I'm having is representing centroids in an efficient way. For example, when we find the mean

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

Hi the list, I need to compute Gower distance between a specific individual and all the other individual. The function DAISY from package cluster compute all the pairwise dissimilarities of a population. If the population is N individuals, that is arround N^2 distances to compute. I need to compute the distance between a specific individual and all the other individual, that is only N

Dear All, I have several socio-economic and geographic variables for the 27 EU countries. I would to use these data to derive a correlation matrix between groups of countries (for a different application). I thought of using kmeans to cluster the groups, and then calibrate between group correlations using distances between the centroids, and within group correlations using distances in a cluster

Hi, I have ~40,000 rows in a database, each of which contains an id column and 20 additional columns of count data. I want to cluster the rows based on these count vectors. Their are ~1.6 billion possible 'distances' between pairs of vectors (cells in my distance matrix), so I need to do something smart. Can R somehow handle this? My first thought was to index the database with

Hello everyone, Image you rsynced your mp3 archive. Later you do some cleanup renaming and start splitting up the directory into a hierarchy and do some file move around. Data-wise you did nothing, meta-data-wise you did a lot. --fuzzy comes into mind for the next rsync. Unfortunately fuzzy matching does not include other (sub-)directories and cares a little too much about modification times for

Dear R community, I would be greatful if somebody could shed light on the following. I have created a set of 6 points to check how centroid agglomeration works in cluster analysis: > Y <- data.frame(x=c(-1,1,1,-1,10,12),y=c(1,1,-1,-1,0,0)) It is quite intuitive to understand that the last clusters to be joined will be {1,2,3,4} with {5,6}. Now, the centroid for the first cluster has

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

hello, i'm pete ,how can i order rows of matrix by max to min value? I have a matrix of membership degrees, with 82 (i) rows and K coloumns, K are clusters. I need first and second largest elements of the i-th row. for example 1 0.66 0.04 0.01 0.30 2 0.02 0.89 0.09 0.00 3 0.06 0.92 0.01 0.01 4 0.07 0.71 0.21 0.01 5 0.10 0.85 0.04 0.01 6 0.91 0.04 0.02 0.02 7 0.00 0.01 0.98 0.00 8 0.02

Hello, I have used the functions agnes and cutree to cluster my data (4977 objects x 22 variables) into 8 clusters. I would like to refine the solution using a k-means or similar algorithm, setting the initial cluster centres as the group means from agnes. However my data matrix has NA's in it and the function kmeans does not appear to accept this? > dim(centres) [1] 8 22 > dim(data)

Hi all I am working on k-means algorithm (in R: kmeans( ) ). The R-help advice us to try several random start in order to avoid local minimum. Does one know if there is a procedure that automaticly run this rerolling and select the best partition ? Or any studies that gives clues on the number of rerolling ? Thanks for helping. Christophe

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 =

https://bugzilla.samba.org/show_bug.cgi?id=10581 Summary: --fuzzy-delay and --fuzzy-limit for fuzzy match tuning Product: rsync Version: 3.1.0 Platform: All OS/Version: All Status: NEW Severity: normal Priority: P5 Component: core AssignedTo: wayned at samba.org ReportedBy: samba at

Dear everybody! In the function hclust, at each stage distances between clusters are recomputed by the Lance-Williams dissimilarity update formula according to the particular clustering method being used. Using "centroid" method, Lance-Williams recurrence formula works properly only for euclidean distance. How is it possible to use properly centroid method with manhattan distance ?

Hello everybody, I'm trying to build a function to illustrate, in 2D, the sequence of the "Single Link" algorithm (the purpose is merely didactic). The idea is to have the scatter of points on a graph. Iteratively, build the segments (with the "segments()" function, for each step). I simulated a data set "d", and created an object "r" using the

https://bugzilla.samba.org/show_bug.cgi?id=12489 Bug ID: 12489 Summary: --fuzzy --fuzzy does not work with daemon Product: rsync Version: 3.1.2 Hardware: All OS: All Status: NEW Severity: critical Priority: P5 Component: core Assignee: wayned at samba.org Reporter:

Hi there, is there any R-function for k-nearest neighbour agglomerative hierarchical clustering? By this I mean standard agglomerative hierarchical clustering as in hclust or agnes, but with the k-nearest neighbour distance between clusters used on the higher levels where there are at least k>1 distances between two clusters (single linkage is 1-nearest neighbour clustering)? Best regards,

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