search for: agglomerating

Hello, In SPSS the cluster analysis output includes an agglomerations schedule, which details the stages when cases are joined. Is it possible to obtain such output when performing cluster analysis in R? If so, I'd appreciate advice regarding how to obtain this information. Any assistance is appreciated, Regards Bob

Dear R-users, I need some help concerning the plotting of dendrograms for hierarchical agglomerative clustering. The agglomeration niveau of each step should be displayed at the branches of the dendrogram. For this I need the x/y coordinates of the branch-agglomerations of the dendrogram. The y-values are known (the heights of the agglomeration), but how can I get the x-values? > mydata

Hi, I'm trying to understand the complete linkage method in hclust. Can anyone provide a breakdown of the formula (p9 of the pdf documentation) or tell me what the "sup" operator does/means? thanks in advance Tom [[alternative HTML version deleted]]

Dear Dario Sacco, >>>>> "DS" == Dario Sacco <dario.sacco at unito.it> >>>>> on Thu, 06 May 2010 17:45:30 +0200 writes: DS> Dear Dr. Maechler, DS> I am an agronomist and a researcher at the University of Turin. I am DS> also teaching "Applied statistics", then I have some knowledge in DS> Statistics, but not

Shown below is most of the FORTRAN subroutine named HCLUST. My question concerns the DO loop labeled as '10'. What happened to its CONTINUE statement? I will assume that after FLAG(I)=.TRUE. is executed that control returns to DO 10 I=1,N. Am I correct? Dave ---------------------------- C Initializations C DO 10 I=1,N C We do not initialize MEMBR in order to be able to

my major is bioinformatics, and i'm trying to cluster ( agglomerate the closest pari of observations ) in R. i have already got my own similarities metric, but do not know how to clust it based on similarities instead of dissimilarities. since the help document of hierclust mentions the parameter "sim", which seems good to me, but it doesn't appear in the code of hierclust()

Dear R users, I have created data for hierarchical agglomerative cluster analysis which consist of the merging pairs and the agglomeration heights, e.g. something like my.merge <- matrix(c(-1,-2,-3,1), ncol=2, byrow=TRUE) my.height <- c(0.5, 1) I'd like to plot a corresponding dendrogram but I don't know how to convert my data to achieve this. Is it possible to create a

my major is bioinformatics, and i'm trying to cluster ( agglomerate the closest pari of observations ) in R. i have already got my own similarities metric, but do not know how to clust it based on similarities instead of dissimilarities. since the help document of hierclust mentions the parameter "sim", which seems good to me, but it doesn't appear in the code of hierclust()

Dear R community, I would be greatful if somebody could shed light on the following. I have created a set of 6 points to check how centroid agglomeration works in cluster analysis: > Y <- data.frame(x=c(-1,1,1,-1,10,12),y=c(1,1,-1,-1,0,0)) It is quite intuitive to understand that the last clusters to be joined will be {1,2,3,4} with {5,6}. Now, the centroid for the first cluster has

Dear Wizards - Thank you so much for your help. That was exactly what I was looking for. Now, I have been working on conditional selection of columns in a data frame. My goal is to calculate the total revenue per sales representative per status in a table. I have come to a complete stop: Let's say, we have a data.frame called df with several columns and a number of rows: df <-

Hi, I'm clustering objects defined by categorical variables with a hierarchical algorithm - average linkage. My distance matrix (general dissimilarity coefficient) includes several distances with exactly the same values. As I see, a standard agglomerative procedure ignores this problems, simply selecting, above equal distances, the one that comes first. For this reason the analysis in output

...egedly) aims to > reduce the TLB misses as it tries to lay out hot functions (or > functions that are likely to be called together near in the final > binary). > IIRC from when I looked at the paper a while ago, it is mostly just a "huffman tree construction" type algorithm (agglomerating based on highest probability) and assumes that if two functions are hot then they are likely to be needed together. This is not always the case. E.g. consider a server that accepts RPC requests and based on those requests either does Foo or Bar which are largely disjoint. It's entirely possibl...

...ries to lay out hot functions (or >>> functions that are likely to be called together near in the final >>> binary). >>> >> >> IIRC from when I looked at the paper a while ago, it is mostly just a >> "huffman tree construction" type algorithm (agglomerating based on highest >> probability) and assumes that if two functions are hot then they are likely >> to be needed together. This is not always the case. >> >> E.g. consider a server that accepts RPC requests and based on those >> requests either does Foo or Bar which are...

Dear all, I am no stat wiz and I am just trying to use the AGNES algorithm at my very modest level of statistical of understanding. I have difficulties understanding the ouput from AGNES. My question is: how to interpret the output, especially how do you I know which cluster solution is the best? In SPSS, an Agglomeration Schedule table is produced and I used to look at the biggest jump between

Dear R users, Thanks to a new contributor, Luigi Cerulo, amap provides 2 more metrics, and another agglomeration linkage for distance computation, and hierarchical clustering. The new centroid linkage method implemented in the cluster3 software tool (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm). In this method the pseudoitem of a cluster is computed as the average of the items

...t;> functions that are likely to be called together near in the final >>>>> binary). >>>>> >>>> >>>> IIRC from when I looked at the paper a while ago, it is mostly just a >>>> "huffman tree construction" type algorithm (agglomerating based on highest >>>> probability) and assumes that if two functions are hot then they are likely >>>> to be needed together. This is not always the case. >>>> >>>> E.g. consider a server that accepts RPC requests and based on those >>>> r...

Hello, I analyse some free-sorting data so I use hierarchical clustering. I want to compare my proximity matrix with the tree representation to evalute the fitting. (stress, cophenetic correlation (pearson's correlation)...) "The cophenetic similarity of two objects a and b is defined as the similarity level at wich objects a and b become members of the same cluster during the course of

I could not figure out what was the default when I ran hclust() without specifying the method. For example: I just have a code like: hclust(dist(data)) Any input would be appreciated:) [[alternative HTML version deleted]]

...y) aims to > reduce the TLB misses as it tries to lay out hot functions (or > functions that are likely to be called together near in the final > binary). > > IIRC from when I looked at the paper a while ago, it is mostly just a "huffman tree construction" type algorithm (agglomerating based on highest probability) and assumes that if two functions are hot then they are likely to be needed together. This is not always the case. > > E.g. consider a server that accepts RPC requests and based on those requests either does Foo or Bar which are largely disjoint. It's entire...

> On Jan 16, 2017, at 1:47 PM, Sean Silva <chisophugis at gmail.com> wrote: > > > > On Mon, Jan 16, 2017 at 1:25 PM, Davide Italiano <davide at freebsd.org <mailto:davide at freebsd.org>> wrote: > On Mon, Jan 16, 2017 at 12:31 PM, Sean Silva via llvm-dev > <llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org>> wrote: > > Do we