@article{scholars6298,
         journal = {International Journal of Business Information Systems},
       publisher = {Inderscience Publishers},
           pages = {55--70},
            year = {2015},
           title = {Data stream outlier detection approach based on frequent pattern mining technique},
          volume = {20},
            note = {cited By 12},
          number = {1},
             doi = {10.1504/IJBIS.2015.070892},
            issn = {17460972},
          author = {Said, A. M. and Dominic, P. D. D. and Faye, I.},
        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. {\^A}{\copyright} 2015 Inderscience Enterprises Ltd.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938685208&doi=10.1504\%2fIJBIS.2015.070892&partnerID=40&md5=5759c3ca9058761889ed05af218e2991}
}