{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:59:46Z","timestamp":1753887586817,"version":"3.41.2"},"reference-count":26,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,3,20]],"date-time":"2021-03-20T00:00:00Z","timestamp":1616198400000},"content-version":"vor","delay-in-days":78,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET\u20104 test were analyzed, and we have obtained the CET\u20104 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit was relatively high. We further improve the algorithm and establish a partition\u2010weighted <jats:italic>K<\/jats:italic>\u2010nearest neighbor algorithm. The K\u2010weighted K nearest neighbor algorithm and the partition algorithm are used in the CET\u20104 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET\u20104 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than <jats:italic>K<\/jats:italic>\u2010nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K\u2010nearest neighbor algorithm classification model. Taking test scores or make\u2010up exams and re\u2010learning as input features, the classification model can effectively predict ordinary students who have not graduated.<\/jats:p>","DOI":"10.1155\/2021\/5577868","type":"journal-article","created":{"date-parts":[[2021,3,20]],"date-time":"2021-03-20T21:20:08Z","timestamp":1616275208000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0516-2044","authenticated-orcid":false,"given":"Hongyan","family":"Wang","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,3,20]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.18280\/isi.250508"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.12677\/ASS.2017.612212"},{"key":"e_1_2_8_3_2","first-page":"1","article-title":"CET-4 score analysis based on data mining technology","author":"Xu J.","year":"2018","journal-title":"Cluster Computing"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899x\/192\/1\/012031"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2014.12.005"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1080\/12269328.2017.1392901"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1136\/heartjnl-2019-315962"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.2106\/jbjs.18.00502"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.biortech.2014.10.107"},{"key":"e_1_2_8_10_2","first-page":"1100","article-title":"Comparative analysis on job prediction of students based on resume using data mining techniques","volume":"7","author":"Kumar T. 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