similar to: Help Graph edit distance in R

Dear R Users I'm trying to acquire a metric for how similar two graphs are by doing inexact graph matching. I heard that the "Graph Edit Distance" is one such metric. Do you know of any R packages (off the top of your head) that implement an algorithm for calculating this from a pair of adjacency matrices? I had a quick skim-look on Cran but couldn't find anything. Cheers Tom

Hey Everyone! I wanted to carry out Hierarchical clustering using distance matrices i have calculated ( instead of euclidean distance etc.) I understand as.dist is the function for this, but the distances in the dendrogram i got by using the following script(1) were not the distances defined in my distance matrices. script: var<-read.table("the distance matrix i calculated",

Hi everyone, I want to create my own distance measure, other than 'euclidean' or 'manhatan', to use in cluster pckgs. To do this I think that I need to change dist(), in mva pckg, or daisy(), in cluster pckg. (or is there a cleaver way ?) But this functions are in fact things like: .Fortran( "daisy", ... ) or .C("dist",...). I tried unsuccessfully to find

Dear friends I reformulate the question. I think I did not formulate it properly. I have some data on some sites. I can define a dissimilarity between each pair of sites. Using this dissimilarity, I have clustered the sites using the hclust algorithm, with method ward. I then obtain 48 clusters, by cutting the tree using cutree with k=48. I would now like to estimate the distance between

Dear friends I reformulate the question. I think I did not formulate it properly. I have some data on some sites. I can define a dissimilarity between each pair of sites. Using this dissimilarity, I have clustered the sites using the hclust algorithm, with method ward. I then obtain 48 clusters, by cutting the tree using cutree with k=48. I would now like to estimate the distance between

i m using a package NbClust for cluster analysis. in the following algorithm ->NbClust(m, diss="NULL", distance = "euclidean", min.nc=2, max.nc=15, method = "ward", index = "all", alphaBeale = 0.1) i want to define my own dissimilarity matrix of dimension 38*38. my original data "m" is a matrix of 365*38. whenever i define my own dissimilarity

I try to perform a clustering using an existing dissimilarity matrix that I calculated using distance (analogue) I tried two different things. One of them worked and one not and I don`t understand why. Here the code: not working example library(cluster) library(analogue) iris2<-as.data.frame(iris) str(iris2) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7

Dear List, is there an elegant (or even not elegant) way how to transform dissimilarity or distance matrix A (or, in general, arbitrary symmetrical matrix) by transposition of rows and columns into a form closest to "block diagonal" matrix B? The matrix A is adjusted the following way A[A<epsilon] <-0 #(epsilon is given "small" number) B: (in its ideal form)

Dear friends, I have several sets of points in a transformed environmental space. Each set of points can be represented as a cloud in the environmental space. This space is spanned by n coordinates, corresponding to the first n PCs of 36 PCs of some environmental variables (12 monthly minimum temperatures, 12 monthly maximum temperature, 12 monthly precipitations). I would like to calculate

On Thu, 11 Feb 2010, Christian Hennig wrote: >It is well know that hierarchical methods are problematic with too large >dissimilarity matrices; even if you resolve the memory problem, the number of >operations required is enormous. There is at least one exception to this. Single-linkage hierarchical clustering with a convex distance such as Euclidean distance is feasible for quite

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

Hi folks, Here is the problem. I am giving an example .I want to find a measure of similarity or dissimilarity among ranking (of students of a same class of size say 50)by two judges. But instead of observing the rank of all the 50 students (Where we could have used rank correlation measures)in each case what I have is 2 list of top 20 students chosen by each judge. The following paper gives out

Hi, I have a set of nodes and a dissimilarity matrix for them, as well as a csv file in which the diss matrix has been converted to [node_1, node_2, dissimilarity] format. I would like to visualize this as a graph in Euclidean space (that is, similar nodes clumped together in clusters), rather than the seriation visualization given by dissplot(). I am using Network WorkBench for my

The documentation for agrep says it uses the Levenshtein edit distance, but it seems to get this wrong in certain cases when there is a combination of deletions and substitutions. For example: > agrep("abcd", "abcxyz", max.distance=1) [1] 1 That should've been a no-match. The edit distance between those strings is 3 (1 substitution, 2 deletions), but agrep matches

Hi all, I've stumbled upon some memory limitations for the analysis that I want to run. I've a matrix of distances between 38000 objects. These distances were calculated outside of R. I want to cluster these objects. For smaller sets (egn=100) this is how I proceed: A<-matrix(scan(file, n=100*100),100,100, byrow=TRUE) ad<-as.dist(A)

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

I am trying to use the cca/rda/capscale functions in vegan to analyse genetic distance data ( provided as a dist object calculated using dist.genpop in package adegenet) with geographic distance partialled out ( provided as a distance object using dist function in veganthis method is attempting to follow the method used by Geffen et al 2004 as suggested by Legendre and . FORTIN (2010). I

Hi, I want to know which distance is using in the function kmeans and if we can change this distance. Indeed, in the function pam, we can put a distance matrix in parameter (by the line "pam<-pam(dist(matrixdata),k=7)" ) but we can't do it in the function kmeans, we have to put the matrix of data directly ... Thanks in advance, Nicolas BOUGET

Dear All, I am in the somewhat unfortunate position of having to reproduce the results previously obtained from (non-metric?) MDS on a "kinship" matrix using Statistica. A kinship matrix measures affinity between groups, and has its maximum values on the diagonal. Apparently, starting with a nxn kinship matrix, all it was needed to do was to feed it to Statistica flagging that the

I posted this yesterday to r-help and Ben Bolker suggested reposting it here... Dickison, Daniel <ddickison <at> carnegielearning.com> writes: > > The documentation for agrep says it uses the Levenshtein edit distance, > but it seems to get this wrong in certain cases when there is a > combination of deletions and substitutions. For example: > > >