Document clustering is useful when large numbers of documents need to be organized for easy access to topically related items, for instance in collections of patent descriptions, medical histories or abstracts, legal precedents, or captioned images, often in hierarchical fashion. Clustering is also useful in exploratory data analysis (e.g., in exploring token occurrences in an unknown language), and indirectly supports various NLP applications because of its utility in improving language models, for instance in providing word clusters to be used for backing off from specific words in cases of data sparsity.