similar to: clustering: single linkage path in minimum spanning tree

Dear R I recently asked for a cluster analysis Using * cluster.results <- hclust(iris.dist, method="complete") * but nothing happened i.e the previous scatterplot matrix still showed up whereas I was expecting a dendogram. Could it be that because I had used cutree before on the scatter plots that it somehow mucked it up. I tried detach then attach and commenced making the data

Dear R community, I am trying to understand how the ward linkage works from a quantitative point of view. To test it I have devised a simple 3-members set: G = c(0,2,10) The distances between all couples are: d(0,2) = 2 d(0,10) = 10 d(2,10) = 8 The smallest distance corresponds to merging 0 and 2. The corresponding ESS are: ESS(0,2) = 2*var(c(0,2)) = 4 ESS(0,10)

Hello List, I am trying to implement a hierarchical cluster using the hclust method agglomerative single linkage method with a small wrinkle. I would like to cluster a set of numbers on a number line only if they are within a distance of 500. I would then like to print out the members of this list. So far I can put a vector: > x<-c(2,10,200,300,600,700) into a distance matrix: >

Hi all, I'm very new to R and read a few tutorials, however I'm having difficulty trying to figure out how to plot a minimum spanning tree. I have a csv file that contains an n-by-n matrix of distances between strains of bacteria called matrix.csv. Looks like: id,strain1, strain2,strain3 strain1,0,.2,.8 strain2,.3,0,.7 strain3,.4,.6,0 I've been messing around with some information

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

Hello all, I am using the clustering functions in R in order to work with large masses of binary time series data, however the clustering functions do not seem able to fit this size of practical problem. Library 'hclust' is good (though it may be sub par for this size of problem, thus doubly poor for this application) in that I do not want to make assumptions about the number of

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,

Hi, I need to find all minimum spanning trees of an unweighted graph. Is there a way in R to do that? Thanks -- View this message in context: http://n4.nabble.com/Minimum-Spanning-Trees-tp1569351p1569351.html Sent from the R help mailing list archive at Nabble.com.

Does anybody know how to do a intermediate linkage clustering in R? Is there a command allready developed? Which package do I have to load? Thanks in advance yvo -- View this message in context: http://www.nabble.com/intermediate-linkage-clustering-tp15736379p15736379.html Sent from the R help mailing list archive at Nabble.com.

Hello the R people. I'd like to calculate shortest distances between cities in one area from cities' coordinates. For this purpose, Splus has a function, "mstree" for calculating minimum spring tree. But for R, I couldn't find similar function. It seems that R has little function for OR(Operational Research). Does anyone know about availability of this method in R?

Dear all, I have an ontology which I would like to load in R to then compute the minimum distance between elements. Since this ontology is not only DAG, the problem is non trivial and therefore decided to use R with the powerful e1071 library (allShortestPaths) to do that. That method requires to provide a distance matrix between the various objects. My ontology contains 32768 objects and

Thanks Mike. My data has longitude and latitude coords and I used distAB {clim.pact} then mst {ape} to calculate my minimum spanning tree. The nodes are telecoms sites from all over Australia. My goal is to determine the minimum cost of linking them via cabling, and I'm starting by calculating the distance "as the crow flies", but will probably eventually need to calculate the

First I just want to say thanks for all the help I've had from the list so far..) I now have what I think is a clustering problem. I have lots of objects which I have measured a dissimilarity between. Now, this list only has one entry per pair, so it is not symmetrical. Example input: NameA NameB Dist 189_1C2 189_1C1 0 189_1C3 189_1C1 0.017 189_1C3 189_1C2 0.017 189_1C4 189_1C1 0

Hi all, I wanted to run the hclust (or any other clustering algorithm) on a distance matrix. I have formed the distance matrix as: distmat: a b c d e a 0.00 0.96 1.60 1.60 1.68 b 0.96 0.00 0.96 1.80 2.64 c 1.60 0.96 0.00 0.84 1.80 d 1.60 1.80 0.84 0.00 0.96 e 1.68 2.64 1.80 0.96 0.00

Thanks a lot, Michael! I cc to R-help, where this question really belongs {as the 'Subject' suggests itself...} -- please drop 'bioconductor' from CC'ing further replies. >>>>> "michael" == michael watson (IAH-C) <michael.watson at bbsrc.ac.uk> >>>>> on Thu, 17 Jun 2004 09:16:59 +0100 writes: michael> OK, admittedly it

Hi everybody, I have a problem with a cluster analysis. I am trying to use hclust, method=ward. The Ward method works with SQUARED Euclidean distances. Hclust demands "a dissimilarity structure as produced by dist". Yet, dist does not seem to produce a table of squared euclidean distances, starting from cosines. In fact, computing manually the squared euclidean distances from cosines

kmeans and clara work great. Thank you for the tip. I have another question: Is it possible to weight the observations in a cluster analysis ? I haven't found any mention of this in the kmeans of clara help texts. Moritz Lennert Charg? de recherche IGEAT - ULB t?l: 32-2-650.65.16 fax: 32-2-650.50.92 email: mlennert at ulb.ac.be > On Wed, 25 Apr 2001, Moritz Lennert wrote: >

Dear R user, I am a biochemist/bioinformatician, at the moment working on protein clusterings by conformation similarity. I only started seriously working with R about a couple of months ago. I have been able so far to read my way through tutorials and set-up my hierarchical clusterings. My problem is that I cannot find a way to obtain information on the rooting of specific nodes, i.e. of

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")