Hi,
I have 10 units with 10 attributes (attr1, attr2, attr3, etc...)
For instance:
unit attr1 attr2 attr3 ...
1 a ww 12
2 a re 11
3 b ww 09
4 c yt 02
5 a qw 02
...
I'd like to answer to the question:
a
with "table" function you can just build a contigence table.
What do you think about "arules" package? I thought "mining associative
rules" is the correct approach to the problem..
Thanks
Abanero
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Hi Ulrich,
I'm studying the principles of Affinity Propagation and I'm really glad to
use your package (apcluster) in order to cluster my data. I have just an
issue to solve..
If I apply the funcion: apcluster(sim)
where sim is the matrix of dissimilarities, sometimes I encounter the
warning
Hi,
I have 1000 monthly time series (just a year) and I want to cluster them.
for instance (x):
jan 2010 feb 2010 mar 2010 apr 2010 ...
ts 1: 12300 12354550 1233 12312 ...
ts 2:23423232 2323 232323 ...
...
My approach is applying clara
Hi,
I'm trying to apply the function daisy() to a data.frame 1x10 but I have
not enough space (error message: cannot allocate vector of length
1476173280).
I didn't imagine I was not able to work with a matrix of just 1
observations... I have setted in Rgui --max-mem-size=2G (I'm not abl
Hi,
I have a 1.000 observations with 10 attributes (of different types: numeric,
dicotomic, categorical ecc..) and a measure M.
I need to cluster these observations in order to assign a new observation
(with the same 10 attributes but not the measure) to a cluster.
I want to calculate for the
Hi,
thank you Joris and Ulrich for you answers.
Joris Meys wrote:
>see the library randomForest for example
I'm trying to find some example in randomForest with categorical variables
but I haven't found anything. Do you know any example with both categorical
and numerical variables? Anyway I
Ulrich wrote:
>Affinity propagation produces quite a number of clusters.
I tried with q=0 and produces 17 clusters. Anyway that's a good idea,
thanks. I'm looking to test it with my dataset.
So I'll probably use daisy() to compute an appropriate dissimilarity then
apcluster() or another meth
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