Frequent pattern-based outlier detection measurements: A survey

Aiman Moyaid Said*, Dhanapal Durai Dominic, Brahim Belhaouari Samir

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Outlier detection is one of the main data mining tasks. The outliers in data are more significant and interesting than common ones in a wide variety of application domains. Recently, a new trend for detecting the outlier by discovering frequent patterns (or frequent itemsets) from the data set has been studies. In this paper, we present a summarization study of the available outlier detection measurements which are based on the frequent patterns discovery.

Original languageEnglish
Title of host publication2011 International Conference on Research and Innovation in Information Systems, ICRIIS'11
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 International Conference on Research and Innovation in Information Systems, ICRIIS'11 - Kuala Lumpur, Malaysia
Duration: 23 Nov 201124 Nov 2011

Publication series

Name2011 International Conference on Research and Innovation in Information Systems, ICRIIS'11

Conference

Conference2011 International Conference on Research and Innovation in Information Systems, ICRIIS'11
Country/TerritoryMalaysia
CityKuala Lumpur
Period23/11/1124/11/11

Keywords

  • frequent pattern mining
  • outlier detection
  • outlier measurement

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