Model-based clustering, e.g. using package mclust will do what you want: it uses normal densities to calculate similarities of objects to clusters, which is a monotone transformation of Mahalanobis distance (basically what's inside the exp() of the multivariate Gaussian density).
If you believe that Mahalanobis distance is the right one for your data, then believing in a multivariate Gaussian model isn't too far-fetched ... Best, Fritz >>>>> On Wed, 3 Mar 2010 16:55:19 -0800 (PST), >>>>> Phil Spector (PS) wrote: > Albyn - > That's a very important fact that I overlooked in my > original response. Thanks for pointing it out. > - Phil > On Wed, 3 Mar 2010, Albyn Jones wrote: >> Note: this procedure assumes that all clusters have the same covariance matrix. >> >> albyn >> >> On Wed, Mar 03, 2010 at 01:23:37PM -0800, Phil Spector wrote: >>> The manhattan distance and the Mahalanobis distances are quite different. >>> One of the main differences is that a covariance matrix is necessary to >>> calculate the Mahalanobis >>> distance, so it's not easily accomodated by dist. There is a function in >>> base R which does calculate the Mahalanobis >>> distance -- mahalanobis(). So if you pass a distance matrix >>> calculated by mahalanobis() to the clustering function, you'll >>> get what you want. >>> - Phil Spector >>> Statistical Computing Facility >>> Department of Statistics >>> UC Berkeley >>> spec...@stat.berkeley.edu >>> >>> >>> On Wed, 3 Mar 2010, Tal Galili wrote: >>> >>>> when you create the distance function to put into the hclust, use: >>>> >>>> dist(x, method = "manhattan") >>>> >>>> >>>> Tal >>>> >>>> >>>> >>>> ----------------Contact >>>> Details:------------------------------------------------------- >>>> Contact me: tal.gal...@gmail.com | 972-52-7275845 >>>> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) | >>>> www.r-statistics.com (English) >>>> ---------------------------------------------------------------------------------------------- >>>> >>>> >>>> >>>> >>>> On Wed, Mar 3, 2010 at 9:14 PM, naama <nw...@technion.ac.il> wrote: >>>> >>>>> >>>>> How can I perform cluster analysis using the mahalanobis distance instead >>>>> of >>>>> the euclidean distance? >>>>> thank you >>>>> Naama Wolf >>>>> >>>>> -- >>>>> View this message in context: >>>>> http://n4.nabble.com/cluster-with-mahalanobis-distance-tp1577038p1577038.html >>>>> Sent from the R help mailing list archive at Nabble.com. >>>>> >>>>> ______________________________________________ >>>>> R-help@r-project.org mailing list >>>>> https://stat.ethz.ch/mailman/listinfo/r-help >>>>> PLEASE do read the posting guide >>>>> http://www.R-project.org/posting-guide.html >>>>> and provide commented, minimal, self-contained, reproducible code. >>>>> >>>> >>>> [[alternative HTML version deleted]] >>>> >>>> ______________________________________________ >>>> R-help@r-project.org mailing list >>>> https://stat.ethz.ch/mailman/listinfo/r-help >>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >>>> and provide commented, minimal, self-contained, reproducible code. >>>> >>> >>> ______________________________________________ >>> R-help@r-project.org mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >>> >> ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
