Citation

Goh, Pey Yun and Tan, Shing Chiang and Cheah, Wooi Ping (2016) Anomaly Detection Using Correctness Matching Through a Neighborhood Rough Set. Lecture Notes in Computer Science, 9949. pp. 434-441. ISSN 0302-9743

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Abstract

Abnormal information patterns are signals retrieved from a data source that could contain erroneous or reveal faulty behavior. Despite which signal it is, this abnormal information could affect the distribution of a real data. An anomaly detection method, i.e. Neighborhood Rough Set with Correctness Matching (NRSCM) is presented in this paper to identify abnormal information (outliers). Two real-life data sets, one mixed data and one categorical data, are used to demonstrate the performance of NRSCM. The experiments positively show good performance of NRSCM in detecting anomaly

Item Type:	Article
Uncontrolled Keywords:	Neighborhood, Rough set, Anomaly detection, Outlier detection
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	27 Jul 2018 16:01
Last Modified:	27 Jul 2018 16:01
URII:	http://shdl.mmu.edu.my/id/eprint/6713

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