{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T13:15:58Z","timestamp":1773926158983,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,9,23]],"date-time":"2020-09-23T00:00:00Z","timestamp":1600819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Outlier detection is critical in many business applications, as it recognizes unusual behaviours to prevent losses and optimize revenue. For example, illegitimate online transactions can be detected based on its pattern with outlier detection. The performance of existing outlier detection methods is limited by the pattern\/behaviour of the dataset; these methods may not perform well without prior knowledge of the dataset. This paper proposes a multi-level outlier detection algorithm (MCOD) that uses multi-level unsupervised learning to cluster the data and discover outliers. The proposed detection method is tested on datasets in different fields with different sizes and dimensions. Experimental analysis has shown that the proposed MCOD algorithm has the ability to improving the outlier detection rate, as compared to the traditional anomaly detection methods. Enterprises and organizations can adopt the proposed MCOD algorithm to ensure a sustainable and efficient detection of frauds\/outliers to increase profitability (and\/or) to enhance business outcomes.<\/jats:p>","DOI":"10.3390\/bdcc4040024","type":"journal-article","created":{"date-parts":[[2020,9,23]],"date-time":"2020-09-23T09:28:08Z","timestamp":1600853288000},"page":"24","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Multi-Level Clustering-Based Outlier\u2019s Detection (MCOD) Using Self-Organizing Maps"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2441-6572","authenticated-orcid":false,"given":"Menglu","family":"Li","sequence":"first","affiliation":[{"name":"Electrical, Computer, and Biomedical Engineering Department, Ryerson University, Toronto, ON M5B 2K3, Canada"}]},{"given":"Rasha","family":"Kashef","sequence":"additional","affiliation":[{"name":"Electrical, Computer, and Biomedical Engineering Department, Ryerson University, Toronto, ON M5B 2K3, Canada"}]},{"given":"Ahmed","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fadlullah, Z., and Khan Pathan, A.S. 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