{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:06:41Z","timestamp":1753884401726,"version":"3.41.2"},"reference-count":22,"publisher":"World Scientific Pub Co Pte Ltd","issue":"14","funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61902159"],"award-info":[{"award-number":["61902159"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the National Philosophy and Social Sciences Foundation of China","award":["19BTQ045"],"award-info":[{"award-number":["19BTQ045"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:p> Machine learning based on genetic algorithms is an important application. The multi-dimensional ordered sample clustering problem is often solved using Fisher\u2019s optimal segmentation method. However, this method has obvious shortcomings when encountering long sample problems due to its high storage requirements during the computation process. Therefore, Fisher\u2019s optimal two-segmentation method is generally used in practical problems instead, which avoids storage problems. But it is prone to local optima. Based on the analysis of the shortcomings of the Fisher optimal segmentation and optimal two-segmentation algorithms, this paper proposes a genetic-based machine learning clustering algorithm, which overcomes the problem of Fisher\u2019s optimal two-segmentation algorithm being prone to local optima and also solves the problem of high storage requirements during the computation process of Fisher\u2019s optimal segmentation method. The application of this algorithm in the optimization system of water environment monitoring points shows that it is effective. <\/jats:p>","DOI":"10.1142\/s0218001424590134","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T09:56:27Z","timestamp":1726221387000},"source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning Clustering Algorithm for Water Environmental Monitoring"],"prefix":"10.1142","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5484-4922","authenticated-orcid":false,"given":"Hongfen","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Road, Changzhou 213001, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0102-7739","authenticated-orcid":false,"given":"Junfeng","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Petroleum Engineering, Changzhou University, No. 21, Gehu Middle Road, Changzhou 213164, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4099-9819","authenticated-orcid":false,"given":"Haixu","family":"Xi","sequence":"additional","affiliation":[{"name":"College of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Road, Changzhou 213001, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6224-5607","authenticated-orcid":false,"given":"Qian","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Road, Changzhou 213001, P.\u00a0R.\u00a0China"}]},{"given":"Xiaoyue","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Road, Changzhou 213001, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5381-6084","authenticated-orcid":false,"given":"Yijun","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Road, Changzhou 213001, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"S0218001424590134BIB001","doi-asserted-by":"publisher","DOI":"10.1162\/evco.1994.2.2.123"},{"key":"S0218001424590134BIB002","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-023-00987-8"},{"key":"S0218001424590134BIB003","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1958.10501479"},{"issue":"4","key":"S0218001424590134BIB004","first-page":"808","volume":"50","author":"Gaofeng L.","year":"2023","journal-title":"Petroleum Explor. 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