{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T01:50:50Z","timestamp":1792461050960,"version":"4.5.0"},"reference-count":22,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Uncertain data mining has become critical due to data generated by sensor networks, RFID systems, and data integration platforms. Mining top-k closed frequent itemsets from uncertain databases is particularly challenging because probabilistic support evaluation is expensive and the search space grows exponentially. Most existing methods rely on depth-first search (DFS) traversal, which explores candidates in enumeration order and often discovers high-support patterns late, leading to weak pruning and costly closure verification. This paper proposes TUFCI, a best-first-search-based algorithm for mining top-k closed frequent itemsets from uncertain databases. TUFCI explores candidates in descending order of probabilistic support using a priority queue, enabling early discovery of strong patterns, rapid threshold elevation, and safe early termination. Support-ordered exploration also improves closure checking by prioritizing supersets most likely to violate the closure property, thereby reducing redundant superset examinations. Experimental results demonstrate that TUFCI significantly outperforms DFS-based approaches in runtime and reduces the number of closure checks, especially on dense datasets.<\/jats:p>","DOI":"10.1371\/journal.pone.0351951","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:46:48Z","timestamp":1781718408000},"page":"e0351951","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Best-first search\u2013based approach for mining top-k closed frequent itemsets from uncertain databases"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5517-9996","authenticated-orcid":true,"given":"Nguyen","family":"Le","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6163-4558","authenticated-orcid":true,"given":"Huy","family":"Vo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7796-4778","authenticated-orcid":true,"given":"Thien","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"pone.0351951.ref001","unstructured":"Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), Santiago, Chile, 1994. 487\u201399. https:\/\/dl.acm.org\/doi\/10.5555\/645920.672836"},{"issue":"6","key":"pone.0351951.ref002","article-title":"Frequent Itemset Mining: A 25 Years Review","volume":"9","author":"JM Luna","year":"2019","journal-title":"WIREs Data Mining and Knowledge Discovery"},{"key":"pone.0351951.ref003","doi-asserted-by":"crossref","unstructured":"Bernecker T, Kriegel H-P, Renz M, Verhein F, Zuefle A. Probabilistic frequent itemset mining in uncertain databases. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009. 119\u201328. https:\/\/doi.org\/10.1145\/1557019.1557039","DOI":"10.1145\/1557019.1557039"},{"issue":"5","key":"pone.0351951.ref004","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/TKDE.2008.190","article-title":"A Survey of Uncertain Data Algorithms and Applications","volume":"21","author":"CC Aggarwal","year":"2009","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"pone.0351951.ref005","first-page":"398","article-title":"Discovering Frequent Closed Itemsets for Association Rules","volume-title":"Lecture Notes in Computer Science","author":"N Pasquier","year":"1999"},{"issue":"5","key":"pone.0351951.ref006","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TKDE.2005.81","article-title":"TFP: an efficient algorithm for mining top-k frequent closed itemsets","volume":"17","author":"Wang Jianyong","year":"2005","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"pone.0351951.ref007","doi-asserted-by":"crossref","unstructured":"Wang J, Han J, Pei J. CLOSET\u00a0: Searching for the Best Strategies for Mining Frequent Closed Itemsets. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003. 236\u201345. https:\/\/doi.org\/10.1145\/956750.956779","DOI":"10.1145\/956750.956779"},{"key":"pone.0351951.ref008","doi-asserted-by":"crossref","unstructured":"Chui CK, Kao B, Hung E. Mining Frequent Itemsets from Uncertain Data. In: Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2007. 47\u201358. https:\/\/doi.org\/10.1007\/978-3-540-71701-0_8","DOI":"10.1007\/978-3-540-71701-0_8"},{"issue":"1","key":"pone.0351951.ref009","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1023\/B:DAMI.0000005258.31418.83","article-title":"Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach","volume":"8","author":"J Han","year":"2004","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"4","key":"pone.0351951.ref010","doi-asserted-by":"crossref","DOI":"10.1002\/widm.1207","article-title":"A Survey of Itemset Mining","volume":"7","author":"P Fournier-Viger","year":"2017","journal-title":"WIREs Data Mining and Knowledge Discovery"},{"issue":"3","key":"pone.0351951.ref011","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1109\/69.846291","article-title":"Scalable algorithms for association mining","volume":"12","author":"MJ Zaki","year":"2000","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"pone.0351951.ref012","doi-asserted-by":"crossref","unstructured":"Sun X, Lim L, Wang S. Mining Probabilistic Frequent Closed Itemsets in Uncertain Databases. In: Proceedings of the 49th Annual ACM Southeast Conference (ACM-SE), 2011. 170\u20135. https:\/\/doi.org\/10.1145\/2016039.2016068","DOI":"10.1145\/2016039.2016068"},{"issue":"1","key":"pone.0351951.ref013","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/TKDE.2015.2458860","article-title":"Efficient Algorithms for Mining Top-K High Utility Itemsets","volume":"28","author":"VS Tseng","year":"2016","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"pone.0351951.ref014","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.knosys.2016.04.016","article-title":"An efficient algorithm for mining the top- k high utility itemsets, using novel threshold raising and pruning strategies","volume":"104","author":"Q-H Duong","year":"2016","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"pone.0351951.ref015","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/TETCI.2020.3000224","article-title":"An Evolutionary Model to Mine High Expected Utility Patterns From Uncertain Databases","volume":"5","author":"U Ahmed","year":"2021","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"4","key":"pone.0351951.ref016","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1109\/TKDE.2019.2942594","article-title":"A Survey of Utility-Oriented Pattern Mining","volume":"33","author":"W Gan","year":"2021","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"pone.0351951.ref017","article-title":"Top-K High Utility Itemset Mining: Current Status and Future Directions","volume":"39","author":"K Singh","year":"2024","journal-title":"The Knowledge Engineering Review"},{"key":"pone.0351951.ref018","doi-asserted-by":"crossref","first-page":"165222","DOI":"10.1109\/ACCESS.2025.3612006","article-title":"Algorithms Based on Dynamic Minimum Probabilistic Support and Utility Thresholds for Mining Top-K High-Utility Itemsets From Uncertain Databases","volume":"13","author":"K Nguyen","year":"2025","journal-title":"IEEE Access"},{"key":"pone.0351951.ref019","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/978-3-032-21628-1_28","article-title":"Efficient extraction of top-k high-utility itemsets under periodicity constraints","volume-title":"Computational Intelligence in Engineering Science: Second International Conference, ICCIES 2026, Nha Trang, Vietnam, April 2\u20134, 2026, Proceedings, Part II","author":"T Nguyen","year":"2026"},{"key":"pone.0351951.ref020","doi-asserted-by":"crossref","unstructured":"Li H, Zhang Y, Zhang N. Discovering Top-K Probabilistic Frequent Itemsets from Uncertain Databases. In: Procedia Computer Science, 2017. https:\/\/doi.org\/10.1016\/j.procs.2017.11.386","DOI":"10.1016\/j.procs.2017.11.482"},{"key":"pone.0351951.ref021","doi-asserted-by":"crossref","first-page":"119156","DOI":"10.1016\/j.eswa.2022.119156","article-title":"ITUFP: A fast method for interactive mining of Top-K frequent patterns from uncertain data","volume":"214","author":"R Davashi","year":"2023","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"pone.0351951.ref022","first-page":"3389","article-title":"SPMF: A Java Open-Source Pattern Mining Library","volume":"17","author":"P Fournier-Viger","year":"2016","journal-title":"Journal of Machine Learning Research"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0351951","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:47:00Z","timestamp":1781718420000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0351951"}},"subtitle":[],"editor":[{"given":"Tai","family":"Dinh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,6,17]]},"references-count":22,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6,17]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0351951","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,17]]}}}