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Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Data Mining and Knowledge Discovery in Databases

Data Mining and Knowledge Discovery in Databases
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Author(s): Ana Azevedo (Polytechnic Institute of Porto, Portugal)
Copyright: 2019
Pages: 13
Source title: Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-7598-6.ch037

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Abstract

The term knowledge discovery in databases or KDD, for short, was coined in 1989 to refer to the broad process of finding knowledge in data, and to emphasize the “high-level” application of particular data mining (DM) methods. The DM phase concerns, mainly, the means by which the patterns are extracted and enumerated from data. Nowadays, the two terms are, usually, indistinctly used. Efforts are being developed in order to create standards and rules in the field of DM with great relevance being given to the subject of inductive databases. Within the context of inductive databases, a great relevance is given to the so-called DM languages. This chapter explores DM in KDD.

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