[R] Why CLARA clustering method does not give the same classes as when I do clustering manually?
Behnam.ABABAEI at limagrain.com
Sun Feb 21 18:20:02 CET 2016
By the way, I have to say that I am dealing with missing values and that is why I am using clara or I may use pam, as kmeans (which is very good at dealing with large datasets) cannot handle missing values.
From: David L Carlson <dcarlson at tamu.edu>
Sent: 21 February 2016 17:55
To: Sarah Goslee; ABABAEI, Behnam
Cc: r-help at r-project.org
Subject: RE: [R] Why CLARA clustering method does not give the same classes as when I do clustering manually?
I do not think this is quite true. When the medoids are not specified, pam/clara looks for a good initial set (build phase) and then finds a local minimum of the objective function (swap phase). Both pam/clara and kmeans can find local minima that are not the global minimum. If the build phase involves any random element, two runs could produce different results. If not, then the original order of the data determines the final result, but the final result is not necessarily the best one possible (assuming the order of the data is irrelevant to the analysis so we are not looking at observations taken along a line in time or space). That is why kmeans includes an argument to run the algorithm multiple times and pick the best result.
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352
From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Sarah Goslee
Sent: Friday, February 19, 2016 1:47 PM
To: ABABAEI, Behnam
Cc: r-help at r-project.org
Subject: Re: [R] Why CLARA clustering method does not give the same classes as when I do clustering manually?
clara() is a version of pam() adapted to use large datasets.
pam() uses the entire dataset, and should give results identical to
your manual procedure, or nearly so. clara() works on subsets of the
data, so it may give a slightly different result each time you run it.
The default parameters for clara() are very small, so you can get
substantially different results from run to run on a large dataset if
you don't change them.
On Fri, Feb 19, 2016 at 6:30 AM, ABABAEI, Behnam
<Behnam.ABABAEI at limagrain.com> wrote:
> I am using CLARA (in 'cluster' package). This method is supposed to assign each observation to the closest 'medoid'. But when I calculate the distance of medoids and observations manually and assign them manually, the results are slightly different (1-2 percent of occurrence probability). Does anyone know how clara calculates dissimilarities and why I get different clustering results?
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