Title
Expressive power of an algebra for data miningExpressive power of an algebra for data mining
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Research group
Department of Mathematics - Computer Sciences
Advanced Database Research and Modeling (ADReM)
Publication type
article
Publication
New York, N.Y.,
Subject
Computer. Automation
Source (journal)
ACM transactions on database systems. - New York, N.Y., 1976, currens
Volume/pages
31(2006):4, p. 1169-1214
ISSN
0362-5915
1557-4644
0362-5915
ISI
000243396000001
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
The relational data model has simple and clear foundations on which significant theoretical and systems research has flourished. By contrast, most research on data mining has focused on algorithmic issues. A major open question is: what's an appropriate foundation for data mining, which can accommodate disparate mining tasks? We address this problem by presenting a database model and an algebra for data mining. The database model is based on the 3W-model introduced by Johnson et al. [ 2000]. This model relied on black box mining operators. A main contribution of this article is to open up these black boxes, by using generic operators in a data mining algebra. Two key operators in this algebra are regionize, which creates regions ( or models) from data tuples, and a restricted form of looping called mining loop. Then the resulting data mining algebra MA is studied and properties concerning expressive power and complexity are established. We present results in three directions: ( 1) expressiveness of the mining algebra; ( 2) relations with alternative frameworks, and ( 3) interactions between regionize and mining loop.
E-info
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https://repository.uantwerpen.be/docman/iruaauth/e32bfa/63492.pdf
Handle