search for: calinsky

I have to solve a clustering problem. My first step is to determinate the number of clusters, that's why I 'm using the Calinski index ( [tr(b)/(k-1)]/[tr(w)/(k-1)] ) which i try to maximize to have the best number of clusters. A function is already implemented in R to calculate this index : clustIndex(cl,x, index="calinski") where cl is the result of a clustering method ,

Hello all, I have a set of data points, which I have pair distances for. I managed to create dendrogram for this data set using diana() in R, however this only gives me the tree and not the clusters themselves. I am trying to determine clusters using Calinsky and Harabasz Index (CH Index). I, however, cannot find how to accomplish this using R. Is there anyone who could help me with this? I searched R documentation for implementation of CH index and found some things but not the clustering. Thanks a bunch. Sincerely, Arif.

Hello, here is my problem, After looking at the mail archives, I found a description of the error I get when I use this package. At first I even tought that they were showing how to solve it. But the thing is that by saying "the programmer forgot drop=FALSE" doesn't show me how I should get rid of the problem I have looked inside the package very quickly and I found three

Hello, Im working on a 50933 point count bird abundance dataset. I've succeeded in calculating a distance matrix for this entire set, but I don't have sufficient memory to convert this to a matrix, as below... abun.dist <- dist(abun.mat[1:50993,1:235) test <- rowMeans(as.matrix(abun.dist)) Error in matrix(0, size, size) : too many elements specified ive been able to run a hclust()

Dear All: I am a newbie to R and as such i am posting this request for help. I am trying to use R to compute the "Calinski Harabasz (CH) Index ". The CH Index is available in the library SAGx. The version of R i am running is 2.2.1. I have my data in a CSV format which i read into R using the read.table() command. After the data has been read i am loading in the "SAGx"

Dear R Users: I am new to R and I am trying to do a cluster analysis on a single continuous variable using the Agnes [Agglomerative Nesting (Hierarchical Clustering) ] in the Package ‘cluster’. I was able to apply this clustering method to my data: ward1 <- Agnes(balances, diss= FALSE, metric = "euclidean", stand = TRUE, method = "ward", keep.diss =TRUE, keep.data =

Hi, I was wondering what the best equivalent to SAS's FASTCLUS and PROC CLUSTER would be. I need to be able to test the significance of the clusters by comparing the probability of obtaining an equal or greater pseudo F to the Bonferroni-corrected level. I will also need to plot r squared against the number of clusters. Thanks so much, Taisa [[alternative HTML version deleted]]

Hello devs. I would like to propose how I plan to go about improving and getting a system that can be integrated into Xapian in this GSoC for the clustering branch. I have identified three areas of work which were not touched last time. 1) Automated Performance Analysis I had roughly implemented 2 evaluation techniques previously (Distance b/w document and centroids within clusters and

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