Thanks David, This is very useful!
-----Original Message-----
From: David Carlson [mailto:dcarl...@tamu.edu]
Sent: Tuesday, August 06, 2013 11:27 AM
To: Li, Yan; r-help@r-project.org
Subject: RE: [R] algorithm for clustering categorical data
What do you mean by representing the categorical fields by 1:k?
a <- c("red", "green", "blue", "orange", "yellow")
becomes
a <- c(1, 2, 3, 4, 5)
That guarantees your results are worthless unless your categories have an
inherent order (e.g. tiny, small, medium, big, giant).
Otherwise it should be four (k-1) indicator/dummy variables (e.g.):
a.red <- c(1, 0, 0, 0, 0)
a.green <- c(0, 1, 0, 0, 0)
a.blue <- c(0, 0, 1, 0, 0)
a.orange <- c(0, 0, 0, 1, 0)
Then you can use Euclidean distance.
-------------------------------------
David L Carlson
Associate Professor of Anthropology
Texas A&M University
College Station, TX 77840-4352
-----Original Message-----
From: Li, Yan [mailto:yan...@ibi.com]
Sent: Tuesday, August 6, 2013 9:36 AM
To: dcarl...@tamu.edu; r-help@r-project.org
Subject: RE: [R] algorithm for clustering categorical data
H David and other R helpers,
If I rescale the numerical fields to [0,1] and represent the categorical fields
to 1:k, which is the same starting point as Gower's measure, but I use
Euclidean distance instead of Gower's distance to do k-means clustering. How
much is the difference? What is the draw back?
Thanks you,
Yan
-----Original Message-----
From: David Carlson [mailto:dcarl...@tamu.edu]
Sent: Thursday, August 01, 2013 12:08 PM
To: Li, Yan; r-help@r-project.org
Subject: RE: [R] algorithm for clustering categorical data
Read up on Gower's Distance measures (available in the ecodist
package) which can combine numeric and categorical data. You didn't give us any
information about how you numerically transformed the categorical variables,
but the usual approach is to create indicator variables that code
presence/absence for each category within a categorical variable. Different
variances between variables can be reduced by standardizing the variables.
-------------------------------------
David L Carlson
Associate Professor of Anthropology
Texas A&M University
College Station, TX 77840-4352
-----Original Message-----
From: r-help-boun...@r-project.org
[mailto:r-help-boun...@r-project.org] On Behalf Of Li, Yan
Sent: Thursday, August 1, 2013 11:00 AM
To: r-help@r-project.org
Subject: [R] algorithm for clustering categorical data
Hi All,
Does anyone know what algorithm for clustering categorical variables? R
packages? Which is the best?
If a data has both numeric and categorical data, what is the best clustering
algorithm to use and R package?
I tried numeric transformation of all categorical fields and doing clustering
afterwards. But the transformed fields have values from 1...10, and my other
fields is in a bigger scale:
10000-...This will make the categorical fields has less effect on the distance
calculation...
Thank you!
Yan
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