Consider the following problem. You have a large dataset, some small subset of which have labels from the classes A, B and C. I would like to classify the unlabelled subset of items each of which can be from classes A, B and C or any other seen class also from other classes I have not seen any labels for yet.

The ideal result would be a full labeling of the unlabelled subset with classes, A, B, C, D, E,

When the unlabal data is given identify the seen classes with the lables and if it's a new unseen class identify that and cluster together the unseen similar images.

Is this an example of semi-supervised classification and what are good approaches one can take to this kind of problem?

Or any implementations that i can use