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These algorithms can further be classified as hard or soft clustering algorithms. Hard clustering computes a hard assignment – each document is a member of exactly one cluster. The assignment of soft clustering algorithms is soft – a document’s assignment is a distribution over all clusters. In a soft assignment, a document has fractional membership in several clusters.<ref name="manning"/>{{rp|499}} [[Dimensionality reduction]] methods can be considered a subtype of soft clustering; for documents, these include [[latent semantic indexing]] ([[truncated singular value decomposition]] on term histograms)<ref>http://nlp.stanford.edu/IR-book/pdf/16flat.pdf</ref> and [[topic model]]s.
Other algorithms involve graph based clustering, [[ontology (information science)|ontology]] supported clustering and order sensitive clustering.
Given a clustering, it can be beneficial to automatically derive human-readable labels for the clusters. [[Cluster labeling|Various methods]] exist for this purpose.
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