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Article type: Research Article
Authors: Hajiloei, Mehdia; * | Jahromi, Alireza Fakharzadeha | Zolmani, Somayehb
Affiliations: [a] Department of OR, Shiraz University of Technology, Iran | [b] Department of Mathematics, Faculty of Science, University of PayamNoor Shiraz, Iran
Correspondence: [*] Corresponding author. Mehdi Hajiloei, Department of OR, Shiraz University of Technology, Iran. Tel/Fax: +987137354501. E-mail: [email protected].
Abstract: Density based methods are significant approaches in outlier detection for high dimensional datasets and Local correlation integral (LOCI) is one of the best of them. To extend LOCI for fuzzy datasets, we should employ suitable metrics to measure the distance between two fuzzy numbers. Euclidean distance measure is a classic one in metric learning, but to overcome curse of dimensionality, we apply fractional distance metric too. Then, after introducing the FLOCI outlier detection algorithm for identifying the fuzzy outliers, we study the efficiency of the proposed method by doing some numerical experiments, in which the obtained results were completely successfull. We also compared the results with Fuzzy versions of Distance based ABOD and SOD methods to prove robustness of this approache. More than the above, one of the main advantages of the new approach is the determination of outlierness factor for each data which is not presented in classical LOCI method.
Keywords: Outlier data, Multi-granularity deviation factor, Triangular fuzzy number, LOCI method, Fractional distance metric
DOI: 10.3233/JIFS-234448
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10805-10812, 2024
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