I apologize in advance if my english isn't too clear. Please feel free to leave a comment and tell me what part doesn't make sense.
I'm currently working on a dataset which contains web data and I have around 7 variables, most of them being categorical ( product name, retailer, retail outlet , etc) and a quantitative variable which represents the price of the product.
I'm trying to apply a clustering algorithm to this data ( hierarchical clustering) in R. First I would like to do a multiple correspondence analysis to visualize the data but I'm not able to do that with the R package FactoMiner because there's too many levels ( ~8000 because this is data for products sold in a entire country) and it cause memory problems.
I don't have experience with working with real life data and the only solutions I've seen online consist of either : -creating dummy variables. I don't think that would be very practical with these many levels - using combine.levels() but I saw this solution in the context of a classification problem and it doesn't make sense for me to use in a clustering problem
Do you know of any solution to this kinds of problems?