similar to: Using centers of hierarchical clustering for k-means

Hi There, I'm working on some cluster analyses on a large data-set using hclust with Wards method and Manhattan (city block) distance measures. I've created dendrograms to illustrate the clustering criteria, but would like to create a plot to examine for the classic elbow criterion to use in determining the best number of clusters. Ideally I'd like to plot percent variance explained

I think are looking for the function 'cutree' from package mva checkout its documentation: > require(mva) > ?cutree pleanty of examples to do what you want. ############################################################################# Hi R lovers! I am using the agnes function of the package cluster to compute a hierarchical clustering. I'd like to know if somebody has ever

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 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,

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

K-means recluster data with given cluster centers Dear R user, I have several large data sets. Over time additional new data sets will be created. I want to cluster all the data in a similar/ identical way with the k-means algorithm. With the first data set I will find my cluster centers and save the cluster centers to a file [1]. This first data set is huge, it is guarantied that cluster

Hello, I am trying to create a heatmap that clusters based on a k-means scheme rather than a hierarchical clustering scheme. Suppose I have the following input data, located in sample.table: x1 x2 x3 x4 x1 17.198 16.306 16.806 16.374 x2 14.554 10.866 15.780 14.596 x3 14.374 14.118 14.569 17.352 x4 17.505 14.596 15.738 14.070 By using the heatmap.2 function as follows, I can create a heatmap

Hi, I need some answers regarding to R. In K-means clustering ,for K=2, How can I find the members of each cluster and What are the coordinates for the cluster centers? Please send me a reply soon. Thank You Sent from Windows Mail [[alternative HTML version deleted]]

Good afternoon, After cuting a hierarchical tree using cutree(), how to check correspondances between classes and branches? This is what we do: srndpchc <- hclust(dist(srndpc$x[1:1000,1:3]),method="ward") #creation of hierarchical tree plclust(srndpchc,hmin=20000) #visualisation srndpchc20000 = cutree(srndpchc,h=20000) #returns 4 classes table(srndpchc20000 ) srndclass20000 =

Hi folks, I am new to the R software. I have been going through different materials to know more about R. I have the R software installed on my windows machine.I would like to know the R source code for the following problems on iris flower data set. I need to do the cluster analysis project with the iris data set. The goal is to cluster the flowers according to their Sepal.Length, Sepal.Width,

Dear R Helpers, I read carefully the documentation and all postings on the hclust and cutree functions, however some aspects of the tree ordering and cluster assignment performed by these functions remain unclear to me, so I would very much appreciate your help in making sure I get them right. Here is an example, with values chosen to illustrate the problems. I have a set of five profiles

Sorry for the long message. I'm doing my best to try to explain myself. I have fitted a spline to my data, I have fitted a spline, filled in the missing data by replicating the spline coefficients associated to the last node. I obtained a number of dendograms by different combination of distance and link-method by calling DIST and AGNES. The agglomerative coefficient is very high (~ 0.99) for

Hi! I just discovered that cutree() and cut.dendrogram() do not assign the same cluster numberings when called on the same tree. More specifically, cutree() assigns cluster numbers by order of appearance in the data, while cut.dendrogram() sorts clusters by height (see example below). I guess this is for historical reasons? I'm hit by this difference when I want to get a vector of cluster

Hi Everyone, I am doing hierarchical clustering analysis and have a question regarding "cutree". I am doing things like this: hc <- hclust(dist(X)) a <- cutree(hc, k=2) Basically "a" is a vector containing the assignments of 1 or 2 for each sample. May I know how "cutree" decides to assign 1 and 2's to each sample (in other words, how clusters 1 and 2

Hi: I have calculated the Silhouette score and Dunn score after hierarchical clustering for 3 clusters: #Distance measure d <- dist(USArrests, method = "euclidean") #Hierarchical clustering hc <- hclust(dist(USArrests), "ave") #calculating silhouette value for 3 clusters sil<- silhouette(cutree(hc, k=3), d) #calculating Dunn index for 3 clusters clus <- cutree(hc,

Dear Dr. Liaw Andy: I have a few more questions about your heatmap function. actually heatmap is what I am looking for. heatmap(x, Rowv, Colv, distfun = dist, hclustfun = hclust, add.expr, scale=c("row", "column", "none"), na.rm = TRUE, ...) my data is a XNEW, > dim(XNEW) [1] 554 335 554 genes, 335 samples. now I want to use 1-CORR as a distance

Hi R users! I have a bit of a problem with using an hierarchical clustering algorithm: a<-c(1:15) b<-rep(seq(1:3), 5) c<-rnorm(15, 0,1) d<-c(sample(1:100, 15, replace=T)) e<-c(sample(1:100, 15, replace=T)) f<-c(sample(1:100, 15, replace=T)) data<-data.frame(a,b,c,d,e,f) q<-data.frame(data$d, data$e, data$f) q<-scale(q) What i want to do is to use an

Hello All, I'm having some trouble figuring out what the clearest way to plot my k-means clustering result on an my existing MDS. First I performed MDS on my distance matrix (note: I performed k-means on the MDS coordinates because applying a euclidean distance measure to my raw data would have been inappropriate) canto.MDS<-cmdscale(canto) I then figured out what would be my optimum

Hi all -- So I'm working through my statistics homework again, and trying to reproduce the examples in the book (Kirk's _Experimental Design_, third edition) in R. This is a completely randomized hierarchical design (CRH-28(A)). The B factor is completely nested within the A factor. Pages 480-482, for those playing along at home. I can use: summary(aov(value ~ a + Error(b),

Well, you don't give much of an example.... I'm replying CC to the R mailing list. Please ask questions there, rather than adressing individuals for basic help. Here is one; does it answer your question ? data(agriculture) ag.ag <- agnes(agriculture) class(ag.ag) pltree(ag.ag) # the dendrogram, if you want to see it ## cut the dendrogram -> get cluster assignments: (ck3 <-