Knowledge maintenance on data streams with concept drifting

Natwichai, Juggapong and Li, Xue (2004). Knowledge maintenance on data streams with concept drifting. In: Jun, Zhang, Ji-Huan, He and Yuxi, Fu, Lecture Notes in Computer Science. Computational And Information Science, Proceedings of the First International Symposium, CIS 2004. First International Symposium on Computational and Information Science (CIS 2004), Shanghai, China, (705-710). 16-18 December 2004. doi:10.1007/b104566

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Author Natwichai, Juggapong
Li, Xue
Title of paper Knowledge maintenance on data streams with concept drifting
Conference name First International Symposium on Computational and Information Science (CIS 2004)
Conference location Shanghai, China
Conference dates 16-18 December 2004
Proceedings title Lecture Notes in Computer Science. Computational And Information Science, Proceedings of the First International Symposium, CIS 2004   Check publisher's open access policy
Journal name Computational and Information Science, Proceedings   Check publisher's open access policy
Place of Publication Berlin; Heidelberg, Germany
Publisher Springer Berlin / Heidelberg
Publication Year 2004
Sub-type Fully published paper
DOI 10.1007/b104566
Open Access Status File (Author Post-print)
ISBN 978-3-540-24127-0
ISSN 0302-9743
1611-3349
Editor Jun, Zhang
Ji-Huan, He
Yuxi, Fu
Volume 3314
Start page 705
End page 710
Total pages 6
Language eng
Abstract/Summary In this paper, we propose a method of hiding sensitive classification rules from data mining algorithms. Our idea is to reconstruct a dataset according to the classification rules that have been checked and agreed by the data owner for release to data sharing. Unlike other heuristic approaches, firstly, our method classifies a given dataset. Then, a set of classification rules are shown to the user. User then identifies the rules that are to be hidden. After that we generate a new decision tree that has only non-sensitive rules. A new dataset can then be reconstructed with no more and no fewer classification rules that can be derived. Our experiments show that this approach is efficient and effective.
Subjects 280109 Decision Support and Group Support Systems
280100 Information Systems
280000 Information, Computing and Communication Sciences
E1
Keyword classification
Concept drifting
Decision tree classifier
Data mining
Q-Index Code E1

 
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Created: Fri, 18 Feb 2005, 10:00:00 EST by Juggapong Natwichai on behalf of School of Information Technol and Elec Engineering