Anomaly detection using correctness matching through a neighborhood rough set

Pey Yun Goh, Shing Chiang Tan, Wooi Ping Cheah

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
EditorsAkira Hirose, Minho Lee, Derong Liu, Kenji Doya, Kazushi Ikeda, Seiichi Ozawa
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319466743
Publication statusPublished - 2016
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9949 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Anomaly detection
  • Neighborhood
  • Outlier detection
  • Rough set

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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