Which clustering method is the standard way to go for text analytics?
in my view, You can use LDA(latent Dirichlet allocation, it is more flexible in comparison to other clustering techniques because of having Alpha and Beta vectors that can adjust to the contribution of each topic in a document and word in a topic. It can help if the documents are not of similar length or quality.
Assume you have lot of text sentences which may have (or not) similarities. Now you want to cluster similar sentences for finding centroids of each cluster. Which method is the prefered way for doing this kind of clustering? K-means with TF-IDF sounds promising. Nevertheless, are there more sophisticated algorithms or better ones? Data structure is tokenized and in a one-hot encoded format.