I have a dataset where some items have been labelled (categorized into 4 classes [A,B,C,D]). However, there is a vast majority of the dataset which has not been labelled. My hypothesis is that there are some characteristics which influence which category is applied to each item. Would clustering or maybe even a recommender system be able to suggest where each item should be placed? On a practical level, would I provide the "labels" within the model? Or would I keep it apart until the end and then overlay those labels on whatever the model managed to group together?
The above example seems like a clustering use case. However, can I spin the problem into a recommender system? As in, you labelled item X as A, and it has characteristics 1,2,3... item Y has similar characteristics, maybe you should label it A as well?