Abstract
In this chapter, we first analyze the state of the art of existing approaches towards focusing solutions and discuss their advantages and disadvantages. We present a unifying framework that covers all existing approaches as instantiations. Reuse of most promising efforts and enhancements to the unifying framework result in a generic algorithm for more intelligent sampling. Its implementation in a commercial data mining tool allows easy applications of different focusing solutions and straightforward integrations of focusing solutions into KDD processes. The primary goal of this chapter is the development of focusing solutions based on existing reusable components.
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.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
(1999). Focusing Solutions. In: Reinartz, T. (eds) Focusing Solutions for Data Mining. Lecture Notes in Computer Science(), vol 1623. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48316-0_4
Download citation
DOI: https://doi.org/10.1007/3-540-48316-0_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66429-1
Online ISBN: 978-3-540-48316-8
eBook Packages: Springer Book Archive