title: Method to cluster data that also has classification labels?
body: <p>I have a dataset in which each line represents a person and their payment behavior during a full year. For each person I have 3 possible classification labels (age, gender, nationality). Payment behavior is defined by over 30 metrics such as number of payments and value of payments. Resulting dataset example looks something like this (I included a few random payment behavior metrics on the right):</p>
  <p><a href="https://i.stack.imgur.com/VOEQb.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/VOEQb.png" alt="enter image description here" /></a></p>
  <p>My goal is to create classes (based on a combination of age/gender/nationality) that represent homogenous groups of people with similar payment behavior. For example: we find that 50-60 year old males from the US all have similar payment behavior. For each class I can then for example determine averages, standard deviations, percentiles etc. Since this seems to be an overlap between clustering and classification, I am stuck in what to research and where to look. Are there any methodologies I can look in to?</p>
  <p>An option I'm thinking of would be to first create all possible classes (e.g. 50-M-US, 50-F-US, 51-M-US, etc.) and then merge them based on Euclidian distances (using all payment behavior metrics means) until a desired number of classes is left. Let me know what you think.</p>