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Data Mining and Knowledge Discovery in Databases

Azevedo, Ana

ISLA — Instituto Politécnico de Gestão e Tecnologia

Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics, 2019 Published by: IGI Global

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.

Keywords

#knowledge discovery #Data mining #Inductive databases #pattern extraction #Knowledge extraction #Relevance (law) #Computer science #Field (mathematics) #Context (archaeology) #Data Science

Bibliographic Details

Publication Type Book chapter

Publication Year 2019

Journal / Conference / Work Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics

ISSN 2327-039X

ISBN 9781522575986

Publisher IGI Global

Publication Status Published

ISLA Authors & Affiliations

Data Mining and Knowledge Discovery in Databases

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Keywords

Bibliographic Details

ISLA Authors & Affiliations