[R] About clustering techniques

[Christian Hennig]

A quick comment on this: imputation is an option to make things 
technically work, but it is not 
necessarily good. Imputation always introduces some noise, ie, it fakes 
information that is not really there.

Whether it is good depends strongly on the data, the situation and the 
imputation method ("random" often not being a very sensible 
choice).

Christian

On Tue, 29 Jul 2008, ctu at bigred.unl.edu wrote:

> Hi Paco,
> I got the same problem with you before. Thus, I just impute the missing 
> values
> For example:
>
> newdata<-as.matrix(impute(olddata, fun="random"))
> then I believe that you could analyze your data.
>
> Hopefully it helps.
> Chunhao
>
>
> Quoting pacomet <pacomet at gmail.com>:
>
>> Hello R users
>> 
>> It's some time I am playing with a dataset to do some cluster 
>> analysis. The
>> data set consists of 14 columns being geographical coordinates and 
>> monthly
>> temperatures in annual files
>> 
>> latitutde - longitude - temperature 1 -..... - temperature 12
>> 
>> I have some missing values in some cases, maybe there are 8 monthly 
>> valid
>> values at some points with four non valid. I don't want to supress the 
>> whole
>> row with 8 good/4 bad values as I wanna try annual and monthy 
>> analysis.
>> 
>> I first tried kmeans but found a problem with missing values. When 
>> trying
>> without omitting missing values kmeans gives an error and when 
>> excluding
>> invalid data too many values are excluded in some years of the data 
>> series.
>> 
>> Now I have been reading about pam, pamk and clara, I think they can 
>> handle
>> missing values. But can't find out the way to perform the analysis 
>> with
>> these functions. As I'm not an statistics nor an R expert the fpc or 
>> cluster
>> package documentation is not enough for me. If you know about a 
>> website or a
>> tutorial explaining the way to use that functions, with examples to 
>> check if
>> possible, please post them.
>> 
>> Any other help or suggestion is greatly appreciated.
>> 
>> Thanks in advance
>> 
>> Paco
>> 
>> --
>> _________________________
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>>
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>> 
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>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide 
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> and provide commented, minimal, self-contained, reproducible code.

*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche