Not tp but I’ve tried using k-means for similar task and and LDA and found LDA to be better. Particularly if your documents have mixture of topics

Well, you don't so much define the groups up front as that you define the number of groups up front, the distance function is what will eventually result in the mapping of inputs to groups if you use the groups (for instance: sample texts in those groups) as a part of the scoring system.

Depending on the task but using K- means implies you have have idea of the clusters you might have ( unsupervised learning). KNN assumes the groups already exist.