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== Clustering v. Classifying ==
Clustering algorithms in computational text analysis groups documents into grouping a set of text what are called subsets or ''clusters'' where the algorithm's goal is to create internally coherent clusters that are distinct from one another.<ref>{{Cite web|url=http://nlp.stanford.edu/IR-book/|title=Introduction to Information Retrieval|website=nlp.stanford.edu|pages=349|access-date=2016-05-03}}</ref> Classification on the other hand, is a form of [[supervised learning]] where the features of the documents are used to predict the "type" of documents.
==See also==▼
*[[Cluster (disambiguation)|Cluster]]▼
*[[Cluster Analysis]]▼
*[[Fuzzy clustering]]▼
== References ==
{{reflist}}
== Bibliography ==
* Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. ''Flat Clustering'' in <u>Introduction to Information Retrieval.</u> Cambridge University Press. 2008
* Nicholas O. Andrews and Edward A. Fox, Recent Developments in Document Clustering, October 16, 2007 [http://eprints.cs.vt.edu/archive/00001000/01/docclust.pdf]
* Claudio Carpineto, Stanislaw Osiński, Giovanni Romano, Dawid Weiss. A survey of Web clustering engines. ACM Computing Surveys, Volume 41, Issue 3 (July 2009), Article No. 17, {{ISSN|0360-0300}}
*Wui Lee Chang, Kai Meng Tay, and Chee Peng Lim, A New Evolving Tree-Based Model with Local Re-learning for Document Clustering and Visualization, Neural Processing Letters, DOI: 10.1007/s11063-017-9597-3. https://link.springer.com/article/10.1007/s11063-017-9597-3
▲==See also==
▲*[[Cluster Analysis]]
▲*[[Fuzzy clustering]]
▲*[[Cluster (disambiguation)|Cluster]]
[[Category:Information retrieval techniques]]
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