{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:10:31Z","timestamp":1777630231176,"version":"3.51.4"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Scientific Research Program Funded by Shaanxi Provincial Education Department","award":["21JK0579"],"award-info":[{"award-number":["21JK0579"]}]},{"name":"Program of Shaanxi Society of technical and Vocational Education","award":["2021SZXYB28"],"award-info":[{"award-number":["2021SZXYB28"]}]},{"name":"Program of Shaanxi Energy Institute","award":["20XJZ04"],"award-info":[{"award-number":["20XJZ04"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the advancement of intelligent campus data acquisition technology, student behavioral data are growing in size, variety, and real-time throughput, posing challenges to the storage capacity and computing power of traditional behavioral data analysis methods. The study focuses on the application of association rule mining in student behavioral data analysis. Data collection, storage, computation, and analysis all comprise integral parts of a four-layer data association mining architecture, and the three-step mining process from \u201cdata preprocessing\u201d to \u201cfinding association rules\u201d to \u201cacquiring relevant knowledge\u201d is described. The existing mining algorithm is updated to address the issues of overscanning of the original dataset and excess iterations. The findings from the case study reveal that the number of iterations in the modified mining algorithm is greatly lessened, effectively improving the mining efficiency of the massive student behavioral dataset.<\/jats:p>","DOI":"10.1007\/s44196-022-00087-4","type":"journal-article","created":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T02:03:34Z","timestamp":1652925814000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Student Behavior Data Analysis Based on Association Rule Mining"],"prefix":"10.1007","volume":"15","author":[{"given":"Tengfei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baorong","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixiao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,19]]},"reference":[{"issue":"2","key":"87_CR1","first-page":"366","volume":"40","author":"LA Yu","year":"2020","unstructured":"Yu, L.A., Zhang, Y.D.: Weight-selected attribute bagging based on association rules for credit dataset classification. Syst. Eng. Theory Pract. 40(2), 366\u2013372 (2020)","journal-title":"Syst. Eng. Theory Pract."},{"issue":"1","key":"87_CR2","first-page":"186","volume":"30","author":"L Cao","year":"2021","unstructured":"Cao, L., Xu, L., Yang, F., Jia, P.F.: Influencing factors analysis of pavement damage based on mining association rules. Comput. Syst. Appl. 30(1), 186\u2013193 (2021)","journal-title":"Comput. Syst. Appl."},{"issue":"2","key":"87_CR3","first-page":"219","volume":"42","author":"QL Hu","year":"2021","unstructured":"Hu, Q.L., Guo, S.: Mining on association rules of atmospheric composite pollutants of urban agglomeration along the Yellow River in Ningxia\u2014a case study of the PM2.5 concentration in the pollution season. J. Ningxia Univ. (Nat. Sci. Ed.) 42(2), 219\u2013225 (2021)","journal-title":"J. Ningxia Univ. (Nat. Sci. Ed.)"},{"issue":"4","key":"87_CR4","first-page":"68","volume":"38","author":"BY Chen","year":"2018","unstructured":"Chen, B.Y., Ding, J., Chen, S.N.: Selection of key incentives for power production safety accidents based on association rule mining. Electr. Power Autom. Equip. 38(4), 68\u201374 (2018)","journal-title":"Electr. Power Autom. Equip."},{"issue":"4","key":"87_CR5","first-page":"95","volume":"36","author":"X Li","year":"2018","unstructured":"Li, X.: Applied research on strong association rules in personalized information push service of smart library. Inf. Sci. 36(4), 95\u201399 (2018)","journal-title":"Inf. Sci."},{"issue":"7","key":"87_CR6","doi-asserted-by":"publisher","first-page":"04020064","DOI":"10.1061\/JTEPBS.0000387","volume":"146","author":"A Ariannezhad","year":"2020","unstructured":"Ariannezhad, A., Wu, Y.J.: Large-scale loop detector troubleshooting using clustering and association rule mining. J. Transp. Eng. A Syst. 146(7), 04020064 (2020)","journal-title":"J. Transp. Eng. A Syst."},{"issue":"2","key":"87_CR7","first-page":"22","volume":"52","author":"B Guo","year":"2020","unstructured":"Guo, B., Li, Z.M., Zhang, J., Yu, Z.W.: Cross-modal crowd sourced data for context-based scenic route recommendation. J. Zhengzhou Univ. (Nat. Sci. Ed.) 52(2), 22\u201328 (2020)","journal-title":"J. Zhengzhou Univ. (Nat. Sci. Ed.)"},{"issue":"8","key":"87_CR8","first-page":"381","volume":"38","author":"JJ Gao","year":"2021","unstructured":"Gao, J.J., Yang, F.: Semi-structured data query optimization algorithm based on swarm intelligence. Comput. Simul. 38(8), 381\u2013385 (2021)","journal-title":"Comput. Simul."},{"issue":"4","key":"87_CR9","first-page":"741","volume":"44","author":"WJ Liang","year":"2021","unstructured":"Liang, W.J., Chen, H., Zhao, S.Y., Li, C.P.: A differentially private scheme for top-k frequent itemsets mining over data streams. Chin. J. Comput. 44(4), 741\u2013760 (2021)","journal-title":"Chin. J. Comput."},{"issue":"11","key":"87_CR10","first-page":"2865","volume":"28","author":"JY Liu","year":"2017","unstructured":"Liu, J.Y., Jia, X.Y.: Multi-label classification algorithm based on association rule mining. J. Softw. 28(11), 2865\u20132878 (2017)","journal-title":"J. Softw."},{"issue":"6","key":"87_CR11","doi-asserted-by":"publisher","first-page":"10781","DOI":"10.3233\/JIFS-201792","volume":"40","author":"X Yu","year":"2021","unstructured":"Yu, X., Zeng, F., Mwakapesa, D.S., Nanehkaran, Y.A., Mao, Y.M.: DBWGIE-MR: a density-based clustering algorithm by using the weighted grid and information entropy based on MapReduce. J. Intell. Fuzzy Syst. 40(6), 10781\u201310796 (2021)","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"1","key":"87_CR12","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s11036-020-01699-w","volume":"26","author":"W Xu","year":"2021","unstructured":"Xu, W., Hoang, V.T.: MapReduce-based improved random forest model for massive educational data processing and classification. Mob. Netw. Appl. 26(1), 191\u2013199 (2021)","journal-title":"Mob. Netw. Appl."},{"issue":"3","key":"87_CR13","doi-asserted-by":"publisher","first-page":"42","DOI":"10.4018\/IJDST.2020070103","volume":"11","author":"S Zerabi","year":"2020","unstructured":"Zerabi, S., Meshoul, S., Boucherkha, S.C.: Models for internal clustering validation indexes based on hadoop-MapReduce. Int. J. Distrib. Syst. Technol. 11(3), 42\u201367 (2020)","journal-title":"Int. J. Distrib. Syst. Technol."},{"issue":"1","key":"87_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-018-0162-3","volume":"6","author":"S Heidari","year":"2019","unstructured":"Heidari, S., Alborzi, M., Radfar, R., Afsharkazemi, M.A., Ghatari, A.R.: Big data clustering with varied density based on MapReduce. Big Data 6(1), 1\u201316 (2019)","journal-title":"Big Data"},{"issue":"5","key":"87_CR15","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1007\/s11704-018-6586-2","volume":"13","author":"Z Wang","year":"2019","unstructured":"Wang, Z., Chen, Q., Suo, B., Pan, W., Li, Z.H.: Reducing partition skew on MapReduce: an incremental allocation approach. Front. Comput. Sci. 13(5), 960\u2013975 (2019)","journal-title":"Front. Comput. Sci."},{"issue":"7","key":"87_CR16","first-page":"31","volume":"44","author":"K Zhu","year":"2017","unstructured":"Zhu, K., Huang, R.Z., Zhang, N.N.: Efficient frequent patterns mining algorithm based on MapReduce model. Comput. Sci. 44(7), 31\u201337 (2017)","journal-title":"Comput. Sci."},{"issue":"04","key":"87_CR17","first-page":"254","volume":"23","author":"MZ Li","year":"2014","unstructured":"Li, M.Z., Ding, Q.X., Wang, Y.P., Lu, T.N.: Performance analysis of management modes of compact disks attached to books in library with chi-square test. Oper. Res. Manag. Sci. 23(04), 254\u2013257 (2014)","journal-title":"Oper. Res. Manag. Sci."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-022-00087-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-022-00087-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-022-00087-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T02:16:03Z","timestamp":1652926563000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-022-00087-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,19]]},"references-count":17,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["87"],"URL":"https:\/\/doi.org\/10.1007\/s44196-022-00087-4","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,19]]},"assertion":[{"value":"28 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest to report regarding the present study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"I certify that this manuscript is original and has not been published and will not be submitted elsewhere for publication while being considered by <i>International Journal of Computational Intelligence Systems<\/i>. And the study is not split up into several parts to increase the quantity of submissions and submitted to various journals or to one journal over time. No data have been fabricated or manipulated (including images) to support your conclusions. No data, text, or theories by others are presented as if they were our own. The submission has been received explicitly from all co-authors. And authors whose names appear on the submission have contributed sufficiently to the scientific work and therefore share collective responsibility and accountability for the results.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical statement"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent statement"}}],"article-number":"32"}}