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Intension Mining

Intension Mining

Héctor Oscar Nigro, Sandra Elizabeth González Císaro
Copyright: © 2005 |Pages: 6
ISBN13: 9781591405603|ISBN10: 1591405602|EISBN13: 9781591407959
DOI: 10.4018/978-1-59140-560-3.ch051
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MLA

Nigro, Héctor Oscar, and Sandra Elizabeth González Císaro. "Intension Mining." Encyclopedia of Database Technologies and Applications, edited by Laura C. Rivero, et al., IGI Global, 2005, pp. 298-303. https://doi.org/10.4018/978-1-59140-560-3.ch051

APA

Nigro, H. O. & González Císaro, S. E. (2005). Intension Mining. In L. Rivero, J. Doorn, & V. Ferraggine (Eds.), Encyclopedia of Database Technologies and Applications (pp. 298-303). IGI Global. https://doi.org/10.4018/978-1-59140-560-3.ch051

Chicago

Nigro, Héctor Oscar, and Sandra Elizabeth González Císaro. "Intension Mining." In Encyclopedia of Database Technologies and Applications, edited by Laura C. Rivero, Jorge Horacio Doorn, and Viviana E. Ferraggine, 298-303. Hershey, PA: IGI Global, 2005. https://doi.org/10.4018/978-1-59140-560-3.ch051

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Abstract

Knowledge discovery is defined as “the non trivial extraction of implicit, unknown, and potentially useful knowledge of the data” (Fayyad, Piatetsky-Shiapiro, Smyth, & Uthurusamy, 1996, p. 6). According to these principles, the knowledge discovery process (KDP) takes the results just as they come from the data (i.e., the process of extracting tendencies or models of the data), and it carefully and accurately transforms them into useful and understandable information. To consider the discovery of knowledge useful, this knowledge has to be interesting (i.e., it should have a potential value for the user; Han & Kamber, 2001).

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