I have a data set with 9000 instances and 40 attributes of mixed data, that is categorical and numeric. My target is to group them into clusters using whichever clustering algorithm works best. I've heard/read that for such a data set Gower distance is suitable. My question is can I combine two (or n) metrics for calculating distances between instances, for example I would like to use let's say Euclidean distance on numeric attributes and Gower distance on categorical attributes. I could always divide my data set into two data sets, one with numeric attributes and the other with categorical. But how could one interpret each result? Summing them up just sounds ... wrong.
My second question is what exactly does Gower distance do with numeric values? Does my first question even make sense?
Here is a snippet of my code, I am using R and functions
df.diss <- daisy(df, metric = "gower", type = list(numeric = c(1, 4, 6, 8, 9, 11, 12, 13, 14, 17 : 37), symm = c(2, 3, 5, 7)), stand = FALSE) df.clust <- agnes(df.diss)
Using these functions or even R is not a must.