search for: agglomeration

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 <- c(1,2,3,4,5) > hc <...

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]]

...w I am working on segmentation of a GIS database. My problem is DS> that I have a set of points over a region and I need to define sub DS> region characterised by small inside variability. DS> The application seems to apply a hierarchical cluster analysis, but the DS> agglomeration procedure should consider only pairs of clusters or of DS> points that are neighbours. DS> This can be performed deleting the dissimilarities in the dissimilarity DS> matrix (for example calculated with the dist() procedure in R) that DS> refers to pairs of points th...

...at 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 restart the C algorithm from a cut. C MEMBR(I)=1. 10 FLAG(I)=.TRUE. NCL=N C C Carry out an agglomeration - first create list of NNs C Note NN and DISNN are the nearest neighbour and its distance C TO THE RIGHT of I. C DO 30 I=1,N-1 DMIN=INF DO 20 J=I+1,N IND=IOFFST(N,I,J) IF (DISS(IND).GE.DMIN) GOTO 20 DMIN=DISS(IND) JM=J...

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 dendrogram object from a cluster hierarchy? Th...

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 coordinates (0,0), while the centroid for the second cluste...

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

On Mon, Jan 16, 2017 at 2:31 PM, Davide Italiano <davide at freebsd.org> wrote: > On Mon, Jan 16, 2017 at 2:07 PM, Mehdi Amini <mehdi.amini at apple.com> > wrote: > > > > 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>

On Mon, Jan 16, 2017 at 3:34 PM, Sean Silva <chisophugis at gmail.com> wrote: > > > On Mon, Jan 16, 2017 at 3:32 PM, Sean Silva <chisophugis at gmail.com> wrote: > >> >> >> On Mon, Jan 16, 2017 at 2:31 PM, Davide Italiano <davide at freebsd.org> >> wrote: >> >>> On Mon, Jan 16, 2017 at 2:07 PM, Mehdi Amini <mehdi.amini at

...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 the error coefficients for each agglomerative steps in order to find where to stop clustering. But with the Agnes output I don't know what I should be looking at. Thanks so much for your help, Aude Aude Plontz Refugee...

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 coordinates, a...

Would it make sense for xray instrumentation be part of -fprofile-generate? PGO will affect inlining decisions etc for the optimized binary, but the collected traces during the instrumented build would still have quite a bit of useful information. -- Sean Silva On Mon, Jan 16, 2017 at 4:33 PM, Xinliang David Li <xinliangli at gmail.com> wrote: > Google GCC records profile data (dynamic

...= euclidean:1 maximum:2 manhattan:3 canberra:4 or binary:5") methodo <- c("euclidean", "maximum", "manhattan", "canberra" ,"binary") dis1 <- dist(fichier , method = methodo[[methode]]) } methode2 <- ask(message ="The agglomeration method to be used = ward:1 single:2 complete:3 average:4 mcquitty:5 median:6 centroid:7") methodo2 <- c("ward", "single", "complete", "average","mcquitty", "median","centroid") hc <- hclust(dis1 , method = methodo2...

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]]

That’s a very good point, the X-ray data that would be useful for the linker should be collected from a "profile-use” build to be accurate. IIUC, that’d look like: build with profile-gen -> run benchmarks -> build with profile-use + Xray -> run benchmarks -> build with profil-use and feed linker with X-ray data for layout optimization. — Mehdi > On Jan 16, 2017, at 8:25

> 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