# [R] Fwd: problem with kmeans

dcarlson at tamu.edu dcarlson at tamu.edu
Tue Apr 29 22:30:52 CEST 2014

```You really should read the instructions before complaining. The
manual page for kmeans clearly states that it works on "a
numeric matrix of data." That is not what you provided. You gave
it a distance matrix. The function pam() will work with a
distance matrix if it is properly labeled as such, but
stringdistmatrix() does not label the output as a distance
matrix:

dis <- stringdistmatrix(test, test, method = "lv")
dis
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
[,12]
[1,]    0    2    6    6    5    2    3    2    6     5     4
5
[2,]    2    0    4    5    5    4    4    2    4     3     4
3
[3,]    6    4    0    1    1    7    7    5    5     3     5
3
[4,]    6    5    1    0    2    7    8    6    6     4     5
4
[5,]    5    5    1    2    0    6    7    6    6     4     4
4
[6,]    2    4    7    7    6    0    1    2    7     5     5
5
[7,]    3    4    7    8    7    1    0    2    6     5     6
5
[8,]    2    2    5    6    6    2    2    0    5     4     5
4
[9,]    6    4    5    6    6    7    6    5    0     2     2
2
[10,]    5    3    3    4    4    5    5    4    2     0     2
0
[11,]    4    4    5    5    4    5    6    5    2     2     0
2
[12,]    5    3    3    4    4    5    5    4    2     0     2
0

require(cluster) # Works once you have installed it.

cl <- pam(dis, 4, diss=TRUE) # Note you must tell pam() that
this is a distance matrix.

print(paste(test, "-", cl\$clustering))
[1] "hematolgy - 1"  "hemtology - 1"  "oncology - 2"   "onclogy
- 2"
[5] "oncolgy - 2"    "dermatolgy - 3" "dermatoloy - 3"
"dematology - 1"
[9] "neurolog - 4"   "nerology - 4"   "neurolgy - 4"
"nerology - 4"

The only apparent error is dermatology which is combined with
hematology but if you look at row 8 of the above distance
matrix, you will see that the Levenshtein distance (the option
you chose) has the value 2 for hematology, hemtology,
dermatolgy, and dermatology. You may want to choose a distance
metric that places greater weight on the initial letter.

Peer reviewed research publications, as opposed to idle gossip,
confirm the accuracy of R.

-----Original Message-----
From: r-help-bounces at r-project.org
[mailto:r-help-bounces at r-project.org] On Behalf Of Peter
Langfelder
Sent: Monday, April 28, 2014 11:44 PM
To: cassie jones
Cc: r-help at r-project.org
Subject: Re: [R] Fwd: problem with kmeans

You are using the wrong algorithm. You want Partitioning around
Medoids (PAM, function pam), not k-means. PAM is also known as
k-medoids, which is where the confusion may come from.

use

library(cluster)

cl = pam(dis, 4)

and see if you get what you want.

HTH,

Peter

On Mon, Apr 28, 2014 at 9:15 PM, cassie jones
<cassiejones26 at gmail.com> wrote:
> Dear R-users,
>
> I am trying to run kmeans on a set comprising of 100
observations. But R
> somehow can not figure out the true underlying groups,
although other
> software such as Jmp, MINITAB are producing the desired
result.
>
> Following is a brief example of what I am doing.
>
> library(stringdist)
> test=c('hematolgy','hemtology','oncology','onclogy',
> 'oncolgy','dermatolgy','dermatoloy','dematology',
> 'neurolog','nerology','neurolgy','nerology')
>
> dis=stringdistmatrix(test,test, method = "lv")
>
> set.seed(123)
> cl=kmeans(dis,4)
>
>
> grp_cl=vector('list',4)
>
> for(i in 1:4)
> {
>     grp_cl[[i]]=test[which(cl\$cluster==i)]
> }
> grp_cl
>
> [[1]]
> [1] "oncology" "onclogy"
>
> [[2]]
> [1] "neurolog" "nerology" "neurolgy" "nerology"
>
> [[3]]
> [1] "oncolgy"
>
> [[4]]
> [1] "hematolgy"  "hemtology"  "dermatolgy" "dermatoloy"
"dematology"
>
> In the above example, the 'test' variable consists of a set of
> terminologies with various typos and I am trying to group the
similar types
> of words based on their string distance. Unfortunately kmeans
is not able
> to replicate the following result that the other software are
able to
> produce.
> [[1]]
> [1] "oncology" "onclogy"  "oncolgy"
>
> [[2]]
> [1] "neurolog" "nerology" "neurolgy" "nerology"
>
> [[3]]
> [1] "dermatolgy" "dermatoloy" "dematology"
>
> [[4]]
> [1] "hematolgy"  "hemtology"
>
>
> Does anyone know if there is a way out, I have heard from a
lot of people
> that multivariate analysis in R does not produce the desired
result most of
> the time. Any help is really appreciated.
>
>
>
>
> Cassie
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
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> https://stat.ethz.ch/mailman/listinfo/r-help
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible
code.

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