> > I had a look at the documentation of the package apcluster. > That's interesting but do you have any example using it with both > categorical > and numerical variables? I'd like to test it with a large dataset.. > Your posting has opened my eyes: problems where both numerical and categorical features occur are probably among the most attractive applications of affinity propagation. So I am considering to include such an example in a future released.
Here is a very crude example (download the imports-85.data from http://archive.ics.uci.edu/ml/machine-learning-databases/autos/ first): > library(cluster) > library(apcluster) > automobiles <- read.table("imports-85.data", header=FALSE, sep=",", > na.strings="?") > sim <- -as.matrix(daisy(automobiles)) > apcluster(sim) The most essential part here is to use daisy() from the package "cluster" for computing distances/similarities. Have a look to the help page of daisy() to get a better impression how it works and how to tailor the distance/similarity calculations to your needs. I do not know whether this is a good data set for clustering. Affinity propagation produces quite a number of clusters. Maybe fiddling with the input preferences is necessary (see Section 4 of vignette of package "apcluster"). Best regards, Ulrich -- View this message in context: http://r.789695.n4.nabble.com/cluster-analysis-and-supervised-classification-an-alternative-to-knn1-tp2231656p2233053.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
