{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T02:00:06Z","timestamp":1767837606581,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T00:00:00Z","timestamp":1575936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004781","name":"Institut Pengurusan dan Pemantauan Penyelidikan, Universiti Malaya","doi-asserted-by":"publisher","award":["PG106-2015B"],"award-info":[{"award-number":["PG106-2015B"]}],"id":[{"id":"10.13039\/501100004781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Data mining is essentially applied to discover new knowledge from a database through an iterative process. The mining process may be time consuming for massive datasets. A widely used method related to knowledge discovery domain refers to association rule mining (ARM) approach, despite its shortcomings in mining large databases. As such, several approaches have been prescribed to unravel knowledge. Most of the proposed algorithms addressed data incremental issues, especially when a hefty amount of data are added to the database after the latest mining process. Three basic manipulation operations performed in a database include add, delete, and update. Any method devised in light of data incremental issues is bound to embed these three operations. The changing threshold is a long-standing problem within the data mining field. Since decision making refers to an active process, the threshold is indeed changeable. Accordingly, the present study proposes an algorithm that resolves the issue of rescanning a database that had been mined previously and allows retrieval of knowledge that satisfies several thresholds without the need to learn the process from scratch. The proposed approach displayed high accuracy in experimentation, as well as reduction in processing time by almost two-thirds of the original mining execution time.<\/jats:p>","DOI":"10.3390\/app9245398","type":"journal-article","created":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T10:52:41Z","timestamp":1575975161000},"page":"5398","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Incremental Algorithm for Association Rule Mining under Dynamic Threshold"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0735-6600","authenticated-orcid":false,"given":"Iyad","family":"Aqra","sequence":"first","affiliation":[{"name":"Department of Information Systems, Faculty of Computer Science &amp; Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0804-3916","authenticated-orcid":false,"given":"Norjihan","family":"Abdul Ghani","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computer Science &amp; Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia"}]},{"given":"Carsten","family":"Maple","sequence":"additional","affiliation":[{"name":"Cyber Security Centre at WMG, University of Warwick, Coventry EC4A 3BZ, UK"},{"name":"The Alan Turing Institute, The British Library, London NW1 2DB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4121-6169","authenticated-orcid":false,"given":"Jos\u00e9","family":"Machado","sequence":"additional","affiliation":[{"name":"ALGORITMI Research Center, University of Minho, 4710-057 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4897-0084","authenticated-orcid":false,"given":"Nader","family":"Sohrabi Safa","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, School of Computing, Electronics and Mathematics, Coventry University, Coventry CV1 5FB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2914","DOI":"10.1016\/j.eswa.2013.09.052","article-title":"An efficient approach for mining cross-level closed itemsets and minimal association rules using closed itemset lattices","volume":"41","author":"Hashem","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_2","first-page":"207","article-title":"Mining association rules between sets of items in large databases. Acm sigmod record","volume":"22","author":"Agrawal","year":"1993","journal-title":"ACM"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1109\/ACCESS.2015.2472355","article-title":"Rule induction-based knowledge discovery for energy efficiency","volume":"3","author":"Chen","year":"2015","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4775","DOI":"10.1007\/s10489-018-1245-8","article-title":"A new framework for metaheuristic-based frequent itemset mining","volume":"48","author":"Djenouri","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ijar.2004.11.006","article-title":"Mining association rules with multiple minimum supports using maximum constraints","volume":"40","author":"Lee","year":"2005","journal-title":"Int. J. Approx. Reason."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.engappai.2014.08.013","article-title":"CCAR: An efficient method for mining class association rules with itemset constraints","volume":"37","author":"Nguyen","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Park, J.S., Yu, P.S., and Chen, M.S. (1997). Mining Association Rules With Adjustable Accuracy, IBM Thomas J. Watson Research Division.","DOI":"10.1145\/266714.266886"},{"key":"ref_8","first-page":"1061","article-title":"Multi-Level Mining and Visualization of Informative Association Rules","volume":"32","author":"Usman","year":"2016","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1007\/s10489-017-1023-z","article-title":"Efficient method for updating class association rules in dynamic datasets with record deletion","volume":"48","author":"Nguyen","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_10","unstructured":"Li, W., Han, J., and Pei, J. (December, January 29). CMAR: Accurate and efficient classification based on multiple class-association rules. Proceedings of the 2001 IEEE International Conference on Data Mining, Washington, DC, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.aei.2014.08.003","article-title":"Efficient updating of discovered high-utility itemsets for transaction deletion in dynamic databases","volume":"29","author":"Lin","year":"2015","journal-title":"Adv. Eng. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2582","DOI":"10.1016\/j.eswa.2014.10.049","article-title":"Association rule mining with mostly associated sequential patterns","volume":"42","author":"Soysal","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_13","unstructured":"Agrawal, R., and Srikant, R. (1994, January 12\u201315). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, San Francisco, CA, USA."},{"key":"ref_14","first-page":"24","article-title":"Predicting Heart Disease by Means of Associative Classification","volume":"16","author":"Ogbah","year":"2016","journal-title":"Int. J. Comput. Sci. Netw. Secur. (IJCSNS)"},{"key":"ref_15","first-page":"1177","article-title":"Voltage thd analysis using knowledge discovery in databases with a decision tree classifier","volume":"6","author":"Leite","year":"2017","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13131","DOI":"10.1109\/ACCESS.2017.2719921","article-title":"Mining human activity patterns from smart home big data for health care applications","volume":"5","author":"Yassine","year":"2017","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1016\/j.cie.2011.11.034","article-title":"Function and service pattern analysis for facilitating the reconfiguration of collaboration systems","volume":"62","author":"Lee","year":"2012","journal-title":"Comput. Ind. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/S0378-7206(01)00091-X","article-title":"Business data mining\u2014A machine learning perspective","volume":"39","author":"Bose","year":"2001","journal-title":"Inf. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.knosys.2014.06.013","article-title":"Principal association mining: an efficient classification approach","volume":"67","author":"Chen","year":"2014","journal-title":"Knowl.-Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kumara, B.T., Paik, I., Siriweera, T., and Koswatte, K.R. (July, January 27). Cluster-based web service recommendation. Proceedings of the 2016 IEEE International Conference on Services Computing (SCC), San Francisco, CA, USA.","DOI":"10.1109\/SCC.2016.52"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.ins.2016.06.036","article-title":"Dependable large scale behavioral patterns mining from sensor data using Hadoop platform","volume":"379","author":"Rashid","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4013","DOI":"10.1002\/sec.1584","article-title":"An intelligent three-phase spam filtering method based on decision tree data mining","volume":"9","author":"Sheu","year":"2016","journal-title":"Secur. Commun. Netw."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gandhi, N., and Armstrong, L.J. (2016, January 14\u201317). A review of the application of data mining techniques for decision making in agriculture. Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, India.","DOI":"10.1109\/IC3I.2016.7917925"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1023\/A:1009773317876","article-title":"Parallel algorithms for discovery of association rules","volume":"1","author":"Zaki","year":"1997","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_25","unstructured":"Li, Z.C., He, P.L., and Lei, M. (2005, January 18\u201321). A high efficient AprioriTid algorithm for mining association rule. Proceedings of the 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Schlegel, B., Karnagel, T., Kiefer, T., and Lehner, W. (2013, January 24). Scalable frequent itemset mining on many-core processors. Proceedings of the Ninth International Workshop on Data Management on New Hardware, New York, NY, USA.","DOI":"10.1145\/2485278.2485281"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"20590","DOI":"10.1109\/ACCESS.2017.2756872","article-title":"Data mining and analytics in the process industry: The role of machine learning","volume":"5","author":"Ge","year":"2017","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1007\/s13042-015-0345-6","article-title":"Iterative sampling based frequent itemset mining for big data","volume":"6","author":"Wu","year":"2015","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Han, J., Pei, J., and Yin, Y. (2000, January 15\u201318). Mining frequent patterns without candidate generation. Proceedings of the 2000 ACM SIGMOD international conference on Management of Data, New York, NY, USA.","DOI":"10.1145\/342009.335372"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"886","DOI":"10.26483\/ijarcs.v9i2.5712","article-title":"FP-growth algorithm based incremental association rule mining algorithm for big data","volume":"9","author":"Ramya","year":"2018","journal-title":"Int. J. Adv. Res. Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3066","DOI":"10.1016\/j.eswa.2008.01.028","article-title":"Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support","volume":"36","author":"Yan","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"321","DOI":"10.3233\/ICA-140467","article-title":"Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm","volume":"21","author":"Luna","year":"2014","journal-title":"Integr. Comput.-Aided Eng."},{"key":"ref_33","unstructured":"Cheung, D.W., Han, J., Ng, V.T., and Wong, C. (March, January 26). Maintenance of discovered association rules in large databases: An incremental updating technique. Proceedings of the Twelfth International Conference on Data Engineering, New Orleans, LA, USA."},{"key":"ref_34","unstructured":"Chang, C.C., Li, Y.C., and Lee, J.S. (2005, January 3\u20134). An efficient algorithm for incremental mining of association rules. Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA\u201905), Tokyo, Japan."},{"key":"ref_35","unstructured":"Bachtobji, M.A., and Gouider, M.S. (2006, January 25\u201328). Incremental maintenance of association rules under support threshold change. Proceedings of the IADIS International Conference on Applied Computing, San Sebastian, Spain."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhou, Z., and Ezeife, C. (2001, January 7\u20139). A low-scan incremental association rule maintenance method based on the apriori property. Proceedings of the Conference of the Canadian Society for Computational Studies of Intelligence, Ottawa, ON, Canada.","DOI":"10.1007\/3-540-45153-6_3"},{"key":"ref_37","unstructured":"(2019, October 15). Integrated & Project Management. Available online: https:\/\/wiki.csc.calpoly.edu\/datasets\/wiki\/apriori."},{"key":"ref_38","unstructured":"(2019, October 15). Frequent Itemset Mining Dataset Repository. Available online: http:\/\/fimi.ua.ac.be\/data\/."},{"key":"ref_39","unstructured":"(2019, October 15). UC Irvine Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu."}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/9\/24\/5398\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:41:06Z","timestamp":1760190066000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/9\/24\/5398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,10]]},"references-count":39,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["app9245398"],"URL":"https:\/\/doi.org\/10.3390\/app9245398","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,10]]}}}