eprintid: 6298
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/62/98
datestamp: 2023-11-09 16:18:04
lastmod: 2023-11-09 16:18:04
status_changed: 2023-11-09 16:05:35
type: article
metadata_visibility: show
creators_name: Said, A.M.
creators_name: Dominic, P.D.D.
creators_name: Faye, I.
title: Data stream outlier detection approach based on frequent pattern mining technique
ispublished: pub
note: cited By 12
abstract: The discovery of the rare data points with distinctive characteristics is one of the significant analysis tasks in data mining. This paper concentrates on the detection of outliers in data stream using frequent pattern mining technique. An outlier measurement is presented and an adaptive method for finding outliers in stream of data is introduced. The results of the empirical studies proved that the proposed approach is effective in detecting outliers' data points. The accuracy comparisons confirmed that the proposed approach is as effective as existing static outlier approach and it outperformed the existing dynamic outlier approach. Moreover, the sensitivity of the proposed approach to the change of data distribution was shown to be effective. © 2015 Inderscience Enterprises Ltd.
date: 2015
publisher: Inderscience Publishers
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938685208&doi=10.1504%2fIJBIS.2015.070892&partnerID=40&md5=5759c3ca9058761889ed05af218e2991
id_number: 10.1504/IJBIS.2015.070892
full_text_status: none
publication: International Journal of Business Information Systems
volume: 20
number: 1
pagerange: 55-70
refereed: TRUE
issn: 17460972
citation:   Said, A.M. and Dominic, P.D.D. and Faye, I.  (2015) Data stream outlier detection approach based on frequent pattern mining technique.  International Journal of Business Information Systems, 20 (1).  pp. 55-70.  ISSN 17460972