Li, Jiuyong "John" - School of Computer and Information Science, University of South Australia

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Li, Jiuyong "John" - School of Computer and Information Science, University of South Australia

Noname manuscript No. (will be inserted by the editor)
TRANSACTIONS ON DATA PRIVACY 1 (2008) 5366 Enhanced P-Sensitive K-Anonymity
Representing Association Classification Rules Mined from Health Data
Using Multiple and Negative Target Rules to Make Classifiers More Understandable
Association Rule Discovery with Unbalanced Class Distributions
Discovery of Functional miRNA-mRNA Regulatory Modules with Computational
Efficient Discovery of Risk Patterns in Medical Data School of Computer and Information Science,
On Optimal Rule Discovery Abstract--In machine learning and data mining, heuristic and association rules are two dominant schemes for rule discovery.
Construct robust rule sets for classification Department of Mathematics
Journal of Intelligent Information Systems, 22:2, 155174, 2004 c 2004 Kluwer Academic Publishers. Printed in The United States.
Robust Rule-Based Prediction Abstract--This paper studies a problem of robust rule-based classification, i.e., making predictions in the presence of missing values
Anonymisation by Local Recoding in Data with Attribute Hierarchical Taxonomies
electronic Journal of Health Informatics http://www.ejhi.net
(, k)-Anonymity: An Enhanced k-Anonymity Model for Privacy-Preserving Data Publishing
Combined Gene Selection Methods for Microarray Data Analysis
Efficiently discovering significant and non-redundant School of Computer &
Mining Risk Patterns in Medical Data Department of Mathematics
A new method of "pharmaco-vigilance" for automatically identifying Adverse Drug Reactions (ADRs) in large populations
Direct Interesting Rule Generation Department of Mathematics and Computing
Discover Dependencies from Data -A Review Jiuyong Li1
Noname manuscript No. (will be inserted by the editor)
An Integrated Model for Next Page Access Prediction
Cloning for Privacy Protection in Multiple Independent Data Publications
Information based data anonymization for classification utility
Satisfying Privacy Requirements Before Data Anonymization