I'm trying to figure out the best way of creating groups in a dataset with many dimensions. I have 1000 measurements, and each measurement has 40 dimensions. The measurements are of neighborhoods with physical and socio-economic data. For example I have measures like the number of people in each area, the population density, the number of buildings, average household size etc, average income.
I'm quite new to this field, so I'm not sure what is the best way of going about this. Two thoughts I had were using CART (specifically the rpart package in R), principal component analysis, and k-means clustering. Ideally I would do all of this analysis in R.
I would like to end up with 6 to 8 "typologies" or representative groupings of the data. They do not necessarily have to include all 40 dimensions.
I would appreciate any advice on the best way of doing this.