Abstract
A number of KDD (Knowledge Discovery in Databases) systems, developed to meet the requirements of many different application domains, has been proposed in the literature. As a result, one can identify several different KDD tasks, depending mainly on the application domain and on the interest of the user. In general, each KDD task extracts a different kind of knowledge from a database, so that each task requires a different kind of KDD algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer Science+Business Media New York
About this chapter
Cite this chapter
Freitas, A.A., Lavington, S.H. (2000). Knowledge Discovery Tasks. In: Mining Very Large Databases with Parallel Processing. The Kluwer International Series on Advances in Database Systems, vol 9. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5521-6_2
Download citation
DOI: https://doi.org/10.1007/978-1-4615-5521-6_2
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7523-4
Online ISBN: 978-1-4615-5521-6
eBook Packages: Springer Book Archive