Abstract
In the seventies, the amount of data typically gathered through collecting or measurements increased so much that the information captured by the data was manageable for humans to utilize it as an aid to decision making. It became obvious that the information needs to be captured in a more succinct and compact way, such as associations, dependencies, class hierarchies, spatial or temporal patterns, in a way more closely reflecting how information is captured in human knowledge. This is particularly true if the data are fused from different sources. As a consequence of that situation, the technology of data mining has developed, which is now experiencing a boom of interest of software producers. Indeed, all main producers of statistical software and several important producers of database systems launched their own data mining tools during the last decade, competed by companies specializing directly in data mining.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Holena, M. (2002). Statistical, Logic Based, and Neural Network Based Methods for Mining Rules from Data. In: Hyder, A.K., Shahbazian, E., Waltz, E. (eds) Multisensor Fusion. NATO Science Series, vol 70. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0556-2_23
Download citation
DOI: https://doi.org/10.1007/978-94-010-0556-2_23
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-0723-1
Online ISBN: 978-94-010-0556-2
eBook Packages: Springer Book Archive