Dear sir, I have dataset of users , I found the similarity among them ,then got similarity network say 100*100 Now, i have to make clustering of users depending on their similarties . but I do not know , from where I have to begin. is k-mean suitable for this case? or newman or ward algorithm . I do appreciate your help with my best regards,, huda ________________________________ Important Notice: the information in this email and any attachments is for the sole use of the intended recipient(s). If you are not an intended recipient, or a person responsible for delivering it to an intended recipient, you should delete it from your system immediately without disclosing its contents elsewhere and advise the sender by returning the email or by telephoning a number contained in the body of the email. No responsibility is accepted for loss or damage arising from viruses or changes made to this message after it was sent. The views contained in this email are those of the author and not necessarily those of Liverpool John Moores University.

Hi, (1) Clippy says: "It looks like your working on a collaborative filtering project" (or something that can be reduced to it) In which case you can start your "hunt" (reading up on the field, etc.) here: http://www.grouplens.org/projects (2) This is the wrong forum for this question as your question (as currently phrased, at least) has little to do with bioinformatics, and even less so with bioconductor. Perhaps an appropriate place to ask questions of what appears to fall in the realm of a machine learning problem would be metaoptimize: http://metaoptimize.com/qa/ But I suggest you do a bit more homework before you post there .. as well as ask a more specific question -- what is your end goal? You say that "k-means" may not be suitable for this case, so I guess you have an idea of what a suitable outcome is -- be more specific about what that might be, ie. how will you know when the end result of whatever black box you stuff your data through (whether it be kmeans or whatever) is a good result? Good luck! -steve On Thu, Aug 2, 2012 at 11:27 AM, Al-Mamovy, Huda <n.n.al-mamovy at="" ljmu.ac.uk=""> wrote: > Dear sir, > I have dataset of users , I found the similarity among them ,then got similarity network > say > 100*100 > Now, i have to make clustering of users depending on their similarties . > but I do not know , from where I have to begin. > is k-mean suitable for this case? > or newman or ward algorithm . > I do appreciate your help > with my best regards,, huda > > ________________________________ > Important Notice: the information in this email and any attachments is for the sole use of the intended recipient(s). If you are not an intended recipient, or a person responsible for delivering it to an intended recipient, you should delete it from your system immediately without disclosing its contents elsewhere and advise the sender by returning the email or by telephoning a number contained in the body of the email. No responsibility is accepted for loss or damage arising from viruses or changes made to this message after it was sent. The views contained in this email are those of the author and not necessarily those of Liverpool John Moores University. > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact