{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T07:12:00Z","timestamp":1766301120841,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T00:00:00Z","timestamp":1718323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This study introduces a novel algorithm tailored for the precise detection of lower outliers (i.e., data points at the lower tail) in univariate datasets, which is particularly suited for scenarios with a single cluster and similar data distribution. The approach leverages a combination of transformative techniques and advanced filtration methods to efficiently segregate anomalies from normal values. Notably, the algorithm emphasizes high-precision outlier detection, ensuring minimal false positives, and requires only a few parameters for configuration. Its unsupervised nature enables robust outlier filtering without the need for extensive manual intervention. To validate its efficacy, the algorithm is rigorously tested using real-world data obtained from photovoltaic (PV) module strings with similar DC capacities, containing various outliers. The results demonstrate the algorithm\u2019s capability to accurately identify lower outliers while maintaining computational efficiency and reliability in practical applications.<\/jats:p>","DOI":"10.3390\/a17060259","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T03:52:53Z","timestamp":1718337173000},"page":"259","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Univariate Outlier Detection: Precision-Driven Algorithm for Single-Cluster Scenarios"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2182-5055","authenticated-orcid":false,"given":"Mohamed Limam","family":"El hairach","sequence":"first","affiliation":[{"name":"LAROSERI, Department of Computer Science, Choua\u00efb Doukkali University, El Jadida 24000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6916-7062","authenticated-orcid":false,"given":"Amal","family":"Tmiri","sequence":"additional","affiliation":[{"name":"National School of Arts and Crafts, Mohammed V University, Rabat 10100, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9339-6934","authenticated-orcid":false,"given":"Insaf","family":"Bellamine","sequence":"additional","affiliation":[{"name":"LAROSERI, Department of Computer Science, Choua\u00efb Doukkali University, El Jadida 24000, Morocco"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12012","DOI":"10.1109\/TKDE.2021.3118815","article-title":"A comprehensive survey on graph anomaly detection with deep learning","volume":"35","author":"Ma","year":"2021","journal-title":"IEEE Trans. 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