similar to: Question about PAM clustering method

Dear R-list members, I am using the cluster package and by the generation of plot.partition I ran into the problem that an alternative title overlaps the default title. > plot.partition(clara.14,which.plot=2,stand=TRUE, main="Silhouette plot of 14 clusters") The manual states that all optional arguments for clusplot.default may also be supplied to plot.partition(). Altering the

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

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

Hello, I have a large data matrix (68x13112), each row corresponding to one observation (patients) and each column corresponding to the variables (points within an NMR spectrum). I would like to carry out some kind of clustering on these data to see how many clusters are there. I have tried the function clara() from the package cluster. If I use the matrix as is, I can perform the clara

Hi all, I am running a series of experiments where after manipulating my data I run several clustering algorithms (agnes, diana and a clustering method of my own) on the data. I wanted to determine which clustering method did the best job, so therefore I had defined my own quality measure using two criteria: compactness of the data within the clusters themselves and the amount of seperation

I don't know if there is some fault in compiling or a bug of the new R-1.7.0 version: cl.pam.2 <- pam(as.dist(1-cor(mel.data)),2) plot(cl.pam.2) perform a right partitioning and silhouette plot on the old R-1.6.2 instead "Error in clusplot.default(x$diss,...... ; x is not numeric" is the output on the new R-1.7.0. Same platform: RH8.0 i686. Some suggestions? A.S.

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, I am using fanny and I have estrange results. I am wondering if someone out there can help me understand why this happens. First of all in most of my tries, it gives me a result in which each object has equal membership in all clusters. I have read that that means "the clustering is entirely fuzzy". Looking at the graphics it is really difficult to understand how objects with so

Hello all, This might have been fixed in later versions (I am using R1.7.0), r-help archive contains messages reporting similar problems but no reports of codes fixes. I have encountered a couple of problems using the silhouette function. one occurs when the clustering contains clusters composed of 1 element (Martin Maechler posted code few months ago that fixes a similar problem that occurs

We use oval to check the system vulnerability. Redhat offer official oval(https://www.redhat.com/security/data/oval/), and it works well on redhat. There is no official centos oval, and using redhat oval on centos got false results. centos is based redhat, so I wrote a script fetch redhat oval files and convert it to useful for centos. And I push the oval to my github:

Bill, Alberto, Gabor, Thank you for answering my question. Now I learned about outer() function. That was a straightforward example. But what if I had a matrix, where the last column was filled with values first (again, a for loop), and the rest was filled by using a double loop? OVal <- matrix(0, n+1, n+1) for(i in 0:n){ OVal[i+1, n+1] <- max(Val[i+1, n+1]-K, 0) } for(i in seq(n,1,

The commit commit 476e84e126171d809f9c0b5d97137f5055f95ca8 Author: Ben Skeggs <bskeggs at redhat.com> Date: Mon Feb 11 09:24:23 2013 +1000 drm/nv50-/disp: initial supervisor support for off-chip encoders changed the write mask in one of the interrupt functions for on-chip encoders, causing a regression in certain VGA dual-head setups. This commit reintroduces the mask

Dear List, I have spent some time playing around with oscap and the RHEL OVAL feed (https://www.redhat.com/security/data/oval/v2/RHEL8/, also check Chapter 16 of the RHEL 8 Design Guide). Because I could not find an existing OVAL file for CentOS, I downloaded one of the RHEL8 files and managed to modify (eg. the rhel-8.1-e4s.oval.xml) it to make it work on a CentOS machine. Basically I just

Hello, I have a large data set that has more columns than rows (sample data below). I am trying to perform a partitioning cluster analysis and then plot that using pca. I have tried using CLUSPLOT(), but that only allows for 'princomp' where I need 'prcomp' as I do not want to reduce my columns. Is there a way to edit the CLUSPLOT() code to use 'prcomp', please? #

Dear all, I am trying (without much success) to use the fuction 'silhouette'. Would anyone encountered that before (or would know where I am wrong ?) Please find below the R ouput. Thanks in advance, L. > s <- silhouette(ct, as.dist(metric)) Error in "[<-"(*tmp*, iC, "sil_width", value = s.i) : number of items to replace is not a multiple of replacement

Does R implements Pseudo-F-Statistics Clustering ? Vogel and Wong derived a formula to evaluate the best clusters number through such a method. Silhouette metod only provides an average evaluation. Thank you very much. Maura tutti i telefonini TIM! [[alternative HTML version deleted]]

Hi, I would like to know if someone knows an extended implementation of k-means in R to find appropriate number of clusters for a given k-dimensional data. Also, I am working on clustering for forecasting, if someone is interested or has knowledge on implementational details please mail me, I would appreciate it. Regards Skanda Kallur "Cogito, ergo sum" (I think, therefore I

I'm fairly new to the silhouette functionality in the cluster package, so apologize if I'm asking something naive. If I run the 'agnes(ruspini)' example from the silhouette section of the cluster package vignette, and assign colours to clusters, two clusters have what appear to be incorrect colours in the silhouette plot. library(cluster) data(ruspini) ar<- agnes(ruspini)

Hi all, once more about the old subj :-) My data has too much various distribution families and for every particular experiment I need just to decide whether the data is "quite homogeneous" or it has two or more clusters. I've revisited the following libraries: amap, clust, cclust, mclust, multiv, normix, survey. And I didn't find any ready-to-use general

Hi list, When I was using 'silhouette' from the 'cluster' package to calculate clustering performances, R crashed. I traced the problem to the fact that my clustering labels only have 2's and 3's. when I replaced them with 1's and 2's, the problem was solved. Is the function purposely written in this way so when I have clustering labels, "2" and