I have a set of data with many samples and many features, but where half of the data is missing one variable (call it A), which is composed of four categories. Based on the half of data which has A, I want to know what category the samples without A would most likely be in if they did have A.
I could build a classifier based on the data with A, and predict the data without A (this is the best route IMHO).
But I'm wondering, out of curiosity, if this method could also be a very, very, very rough way of doing something similar:
Cluster the data which has A into the same number of clusters as categories in A (in this case four).
Check for an an association between the clusters and the categories in A (using a frequency table and chi-square test).
If there is an association, run the data without A through the clustering model to figure out what category of A it's most likely associated with (based on what cluster it is in).