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Cluster hypothesis
In machine learning and information retrieval, the cluster hypothesis is an assumption about the nature of the data handled in those fields, which takes various forms. In information retrieval, it states that documents that are clustered together "behave similarly with respect to relevance to information needs".〔http://nlp.stanford.edu/IR-book/html/htmledition/clustering-in-information-retrieval-1.html〕 In terms of classification, it states that if points are in the same cluster, they are likely to be of the same class.〔O. Chapelle and B. Schölkopf and A. Zien, Semi-Supervised Learning, MIT Press, 2006〕 There may be multiple clusters forming a single class.
== Information retrieval ==
Search engines may cluster documents that were retrieved for a query, then retrieve the documents from the clusters as well as the original documents. Alternatively, search engines may be ''replaced'' by browsing interfaces that present results from clustering algorithms. Both these approaches to information retrieval are based on a variant of the cluster hypothesis, that documents that are similar by a clustering criterion (typically term overlap) will have similar relevance to users' information needs.〔
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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