search for: mediods

Yes, that seems like a sensible idea to me. Terry Therneau On Sat, 2011-12-03 at 12:00 +0100, r-help-request at r-project.org wrote: > The problem: There are no a priori groupings to run a classification > on > > My solution: > > This is a non-R code question, so I appreciate any thoughts. I have > used pam in the cluster package proceeded by sillohouette to find the

Hi, I need some help understanding the output from PAM. When I look at the output it doesn't list the cluster number by the median vlaues on each of the variables (like it does with k-means) Instead I have the following: So I know for instance cluster 1 has a mean for variable1 of 33.33, however when I run PAM i get: variable 1 variable2 29 32 12 97 12

Dear list, I have two dissimilarity matrices, one for a training data set which I then clustered using PAM. The second is a diss matrix for a validation data set (an independent field sample). I have been trying to use knn to distinguish distances between the validation data set and the 6 mediods of the training data defined by using PAM. I continue to get error messages in regards to either the lengths of matrices being different or that the dimensions of my data are incorrect. Here are the two steps I am using. I define my training data set: #read in the data file comm.data = read.csv(c...

I am using the PAM algorithm in the CLUSTER library. When I allow PAM to seed the medoids using the default __build__ algorithm things work well: > pam(stats.table, metric="euclidean", stand=TRUE, k=5) But I have some clusters from a Hierarchical analysis that I would like to use as seeds for the PAM algorithm. I can't figure what the mediod argument wants. When I put in the

I am attempting to write a routine where I can run PAM (partition around mediods) on a dataset containing multiple soil cores and PCA spectral data from several depths per core. I want to run PAM on each individual core, so I need to group the data by core to run the analysis. Below is an example of my data structure: Lab.id PC1 PC2 PC3 MAT057.2.5 2.438454966 -1.011182986 -3...

...,veg_nmds$points,1,2,3) My issue is that I would like to use the points3d function to add points of different colors and sizes to the dynamic 3D plot created by using ordirgl. In my case the different colored and sized points represent different clusters from the results of the Partitioning Around Mediods (pam) clustering function (from library cluster). I have used this with success in the past (two years back), but can't get it to work properly now. An example of the code I have used in the past is: > points3d(veg_nmds$points[,1],veg_nmds$points[,2],veg_nmds$points[,3],display = "si...

Dear all, I'm trying to estimate clusters from a very large dataset using clara but the program stops with a memory error. The (very simple) code and the error: mydata<-read.dbf(file="fnorsel_4px.dbf") my.clara.7k<-clara(mydata,k=7) >Error: cannot allocate vector of size 465108 Kb The dataset contains >3,000,000 rows and 15 columns. I'm using a windows computer

Hi I'm trying to cluster a continuous dataset with a varying number of clusters and with a restriction that each cluster must have more than 'x' number of observations. I have tried the clara function, using silhouette to give me the neighbouring cluster mediod of each observation, then merging an observation from a cluster with less than 'x' obs. into its' neighbour,

...ind problems. I formed clusters using the following code: s<-sample(txn,1000) d<-dissimilarity(s, method="Jaccard") clustering<-pam(d,k=5) But when I'm trying to predict this on the larger set it keeps throwing an subscript out of bound error Label<-predict(s[clustering$mediods],txn,method="Jaccard") Can anyone explain to me why this keeps happening ?? I've tried this on other datasets like Groceries/ Adult in the arules package and it seems to work fine !! Thanks, Ankur [[alternative HTML version deleted]]