Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Rough set algorithms in classification problem
Rough set methods and applications
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Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
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PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Discovering cluster-based local outliers
Pattern Recognition Letters
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
A New Interestingness Measure of Association Rules
WGEC '08 Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing
An Effective Algorithm for Mining Positive and Negative Association Rules
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
Outlier detection based on rough sets theory
Intelligent Data Analysis
An information entropy-based approach to outlier detection in rough sets
Expert Systems with Applications: An International Journal
Multiple attribute frequent mining-based for dengue outbreak
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
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This paper discusses on the detection of outliers by hybriding Rough_Outlier Algorithm with Negative Association Rules. An optimization algorithm named Binary Particle Swarm Optimization is used to improve the computation of Non_Reduct in order to detect outliers. By using Binary PSO algorithm, the rules generated from Rough_Outliers algorithm is optimized, giving significant outliers object detected. The detection of outliers process is then enhanced by hybriding it with Negative Association Rules. Frequent and Infrequent item sets from outlier rules are generated. Results show that the hybrid Rough_Negative algorithm is able to uncover meaningful knowledge of outliers from the frequent and infrequent item sets. These knowledge can then be used by experts in their field of domain for better decision making.