similar to: Clustering algorithms don't find obvious clusters

Hello all! I am performingsome clustering analysis on microarray data using agnes{cluster} and I have created my own dissimilarity matrix according to a distance measure different from "euclidean" or "manhattan" etc. My question is, if I choose for example method="complete", how are the distances between the elements calculated? Are they taken form the dissimilarity

Hello, In the documentation for agnes in the package 'cluster', it says that NAs are allowed, and sure enough it works for a small example like : > m <- matrix(c( 1, 1, 1, 2, 1, NA, 1, 1, 1, 2, 2, 2), nrow = 3, byrow = TRUE) > agnes(m) Call: agnes(x = m) Agglomerative coefficient: 0.1614168 Order of objects: [1] 1 2 3 Height (summary): Min. 1st Qu. Median Mean 3rd

Hi everyone! I would appreciate help with using decompose.graph(), community detection functions from igraph and lapply(). I have an igraph object G with vertex attribute "label" and edge attribute "weight". I want to calculate community memberships using different functions from igraph, for simplicity let it be "walktrap.community". This graph is not connected,

Hi all, I'm trying to perform a hierarchical clustering on some dissimilarity data that I have but the data matrix I have already contains the dissimilarity values. These values are calculated using a separate program. The dissimilarity matrix in complete with no missing values but the hclust, and agnes routines require it in the form produced by daisy or dist. Is there any of converting

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

Hi, I'm mighty new to R. I'm using it on Windows. I'm trying to cluster using a distance matrix I created from the data on my own and called it D10.dist. I loaded the cluster package. Then tried the following command... > agnes("E:D10.dist", diss = TRUE, metric = "euclidean", stand = FALSE, > method = "average", par.method, keep.diss = n < 1000,

Hi group, My case has N physicians with each seeing M patients. One physician could have seen a group of patients, or, a patient could have been seen by multiple number of physicians. In order words, there are overlaps. Now, I have the following NxM matrix Patient#1 Patient#2 Patient#3 ....... Patient#m Physician#1 1 0 1 ....... 0 Physician#2

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

Hi Adam, > One problem, I was trying to solve was, that I need to declare variables of let's say 5-bit width like 'i5 var', > the maximal bit-width may be limited to 64 bits. I need such variables to represent instruction's operands, > example is at the end this message. any standard compliant C compiler supports i5, believe it or not. Try this: #include

Hi Adam, John is right, the TCE stuff would be useful for you. Our compiler targets a processor template that the designer can populate pretty freely. The compiler then reads the architecture description and creates an LLVM backend on the fly. Please don't hesitate to get in touch with us if you have questions. -- Pertti

Hello everybody Anyone, to know a gateway that works with nextel simm cards? I'm looking for them, in internet, but I did'n look. Best regards -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.digium.com/pipermail/asterisk-users/attachments/20080129/0527ba52/attachment.htm

Hi all, I have a dataset with numeric and factor columns of data which I developed a Gower Dissimilarity Matrix for (Daisy) and used Agglomerative Nesting (Agnes) to develop 20 clusters. I would like to use the 20 clusters to determine cluster membership for a new dataset (using predict) but cannot find a way to do this (no way to "predict" in the cluster package). I know I can use

On Thu, 04 Jun 2009 22:55:04 +0200, Pertti Kellomäki <pertti.kellomaki at tut.fi> wrote: > Hi Adam, > > John is right, the TCE stuff would be useful for you. Our > compiler targets a processor template that the designer can > populate pretty freely. The compiler then reads the architecture > description and creates an LLVM backend on the fly. > > Please don't

Dear All, I have a dissimilarity matrix which I happily convert to a distance object by running: X <- as.dist(Y) and I can happily now run either hclust(X) or agnes(X). So that the various bits of output are labelled correctly I would dearly like to be able to give names to the columns and rows of X, as would happen if I ran: mydata<-read.table("clipboard",header=T)

Hi, exist a special cluster-analysis algorithms which can work with NA's. a further "problem" is that i want cluster variables not cases to identify special variable-set's. Is it a common way turn the data.frame and use kmeans,because this works with NA's, or have anybody another method for finding "variable-sets" , with exception of factor analysis. thanks for

Hi, I'd got a matrix of correaltion values. Which cluster method can I use to cluster it? Best Regards...

I haven't read the book, but could anyone explain more about this parameter? help(agnes) says that ac measures the amount of clustering structure found. From the definition given in help(agnes.object), however, it seems that as long as the dissimilarity of the merger in the final step of the algorithm is large enough, the ac value will be close to 1. So what does ac really mean? Thank

Hello gyus, I am working on a project, where we are trying to create a development environment for new ASIP processor design. Part of this project is a compiler generator, where we would like to generate C compiler from some instruction description. To keep it short, let's say, that in each instruction's semantics is described by some C code. What I would like to do is to compile this

Dear R-helpers, Is there somebody who knows if R has already a build in function for Hierarchical Clustering which uses Mutual Information as proximity measure? Many thanks and best regards, J. --------------------------------- [[alternative HTML version deleted]]

Dear mailing group, I have loaded an Excel file into R by calling it ".csv" and using the "read.csv" function in R. However then I want to use the (limma package specific, I believe) function "hclust", which clusters data in a tree dendrogram, by similarity. However, I receive the errors msg.s: 1) "missing observations in cov/cor" 2) "In