I am looking for document clustering approaches which gives high recall. I tried looking at Google but all I get is TF-IDF and K-means. Are there more sophisticated approaches than that which achieve a high recall?
Edit as per suggestions:
I have tried employing LDA using gensim. The results are rather bad. I removed the stop words, did stemming and also removed all punctuation. However when I try to infer a topic in the test, the most probable topic of each document is almost the same (>98% of the times). I have tried with 50, 100, 200, 300 and 400 topics, all give same results.
Attached is the distribution of 200(orange) topics and 300(blue) topics. (Sorry about the wrong title.)