# Semi Supervised Learning without label propagation

I am trying to cluster some words by affinity. Using Word2Vec I obtained vector representation of every word that I can cluster with a normal unsupervised method.

Of these words, though, I know the classification of some of them, for example I know:

Colors group ==> red, blue, yellow
Gender group ==> man, woman

UNKNOWN ==> shoes, brown, beautiful.

As the example shows, brown should be categorized as a Color, but shoes and beautiful should be different clusters.

How could I use this information to create a semi-supervised model in order to cluster every word?

While the question is generic, I actually tried to create a program in Python, I tried the scikit methods sklearn.semi_supervised.LabelSpreading and sklearn.semi_supervised.LabelSpreading.
These, though, are not what I need since they only assign the known labels to the remaining objects, meaning that I would end up only with my Colors and Gender group.