okay,
when you cluster information, you can have two inputs
raw data information which the algorithms converts have into a matrix and
then processes
a pre-processed matrix which you create yourself to input into a package
essentially, packages will have a default assumption about the data you are
using or the type of matrix you are using
these matrices are often defined in simplistic terms as either a similarity
or dissimilarity matrix
think of a correlation matrix as an example of a matrix which represents
similarity
i think you will need to create a dissimilarity matrix (think of something
that is like a correlation matrix which measures similarity in the
diagonals) and it is the opposite of this (technically not correct, but you
get the idea I hope)
i use clustan graphics for all my clustering needs and gower's coefficient
is the input i use when i have mixed variables
if you pre-process (create a dissimilarity matrix) using Gowers algorithm,
then specify this everything should work fine
once you get this sorted, it should be all straight-forward
PD
----- Original Message -----
From: "Chua Siang Li" <[EMAIL PROTECTED]>
To: <r-help@r-project.org>
Sent: Wednesday, June 18, 2008 7:46 PM
Subject: [R] Cluster on both categorical and numerical data
Hello there. Is there any function in R that can do cluster on a set of
data that has both categorical and numerical variables? thanks.
siangli
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