{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:08:33Z","timestamp":1760710113141,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T00:00:00Z","timestamp":1579737600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T00:00:00Z","timestamp":1579737600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1007\/s10489-019-01622-1","type":"journal-article","created":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T22:02:17Z","timestamp":1579816937000},"page":"1487-1497","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Mining top-k frequent patterns from uncertain databases"],"prefix":"10.1007","volume":"50","author":[{"given":"Tuong","family":"Le","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2723-1138","authenticated-orcid":false,"given":"Bay","family":"Vo","sequence":"additional","affiliation":[]},{"given":"Van-Nam","family":"Huynh","sequence":"additional","affiliation":[]},{"given":"Ngoc Thanh","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Sung Wook","family":"Baik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,23]]},"reference":[{"key":"1622_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: SIGMOD\u201993, pp 207\u2013216","DOI":"10.1145\/170035.170072"},{"issue":"2","key":"1622_CR2","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1002\/widm.1181","volume":"6","author":"T Le","year":"2016","unstructured":"Le T, Vo B (2016) The lattice-based approaches for mining association rules: a review. WIREs Data Mining and Knowledge Discovery 6(2):140\u2013151","journal-title":"WIREs Data Mining and Knowledge Discovery"},{"issue":"4","key":"1622_CR3","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/TKDE.2005.60","volume":"17","author":"MJ Zaki","year":"2005","unstructured":"Zaki MJ, Hsiao CJ (2005) Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Trans Knowl Data Eng 17(4):462\u2013478","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1622_CR4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.datak.2014.11.004","volume":"95","author":"SJ Nanda","year":"2015","unstructured":"Nanda SJ, Panda G (2015) Design of computationally efficient density-based clustering algorithms. Data Knowl Eng 95:23\u201338","journal-title":"Data Knowl Eng"},{"issue":"4","key":"1622_CR5","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3390\/sym10040079","volume":"10","author":"T Le","year":"2018","unstructured":"Le T, Lee MY, Park JR, Baik SW (2018a) Oversampling techniques for bankruptcy prediction: novel features from a transaction dataset. Symmetry 10(4):79","journal-title":"Symmetry"},{"issue":"7","key":"1622_CR6","doi-asserted-by":"crossref","first-page":"250","DOI":"10.3390\/sym10070250","volume":"10","author":"T Le","year":"2018","unstructured":"Le T, Le HS, Vo MT, Lee MY, Baik SW (2018b) A cluster-based boosting algorithm for bankruptcy prediction in a highly imbalanced dataset. Symmetry 10(7):250","journal-title":"Symmetry"},{"key":"1622_CR7","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.ins.2019.04.060","volume":"494","author":"T Le","year":"2019","unstructured":"Le T, Vo B, Fujita H, Nguyen NT, Baik SW (2019a) A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting. Inf Sci 494:294\u2013310","journal-title":"Inf Sci"},{"key":"1622_CR8","doi-asserted-by":"crossref","unstructured":"Le T, Vo MT, Vo B, Lee MY, Baik SW (2019b) A hybrid approach using oversampling technique and cost-sensitive learning for bankruptcy prediction. Complexity, ID 8460934","DOI":"10.1155\/2019\/8460934"},{"issue":"1","key":"1622_CR9","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3390\/sym11010089","volume":"11","author":"T Le","year":"2019","unstructured":"Le T, Baik SW (2019) A robust framework for self-care problem identification for children with disability. Symmetry 11(1):89","journal-title":"Symmetry"},{"issue":"4","key":"1622_CR10","first-page":"155","volume":"5","author":"N Indurkhya","year":"2015","unstructured":"Indurkhya N (2015) Emerging directions in predictive text mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5(4):155\u2013164","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"issue":"16","key":"1622_CR11","doi-asserted-by":"crossref","first-page":"7653","DOI":"10.1016\/j.eswa.2014.06.009","volume":"41","author":"AK Nassirtoussi","year":"2014","unstructured":"Nassirtoussi AK, Aghabozorgi SR, The YW, Ngo DCL (2014) Text mining for market prediction: a systematic review. Expert Syst Appl 41(16):7653\u20137670","journal-title":"Expert Syst Appl"},{"key":"1622_CR12","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ijar.2016.09.006","volume":"80","author":"MD Ruiz","year":"2017","unstructured":"Ruiz MD, G\u00f3mez-Romero J, Molina-Solana M, Ros M, Mart\u00edn-Bautista MJ (2017) Information fusion from multiple databases using meta-association rules. Int J Approx Reason 80:185\u2013198","journal-title":"Int J Approx Reason"},{"issue":"3","key":"1622_CR13","first-page":"87","volume":"5","author":"S Vairavasundaram","year":"2015","unstructured":"Vairavasundaram S, Varadharajan V, Vairavasundaram I, Ravi L (2015) Data mining-based tag recommendation system: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5(3):87\u2013112","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"key":"1622_CR14","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.ins.2019.03.050","volume":"489","author":"P Fournier-Viger","year":"2019","unstructured":"Fournier-Viger P, Li Z, Lin JCW, Kiran RU, Fujita H (2019) Efficient algorithms to identify periodic patterns in multiple sequences. Inf Sci 489:205\u2013226","journal-title":"Inf Sci"},{"key":"1622_CR15","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.knosys.2017.12.003","volume":"143","author":"W Gan","year":"2018","unstructured":"Gan W, Lin JCW, Fournier-Viger P, Chao HC, Fujita H (2018) Extracting non-redundant correlated purchase behaviors by utility measure. Knowl-Based Syst 143:30\u201341","journal-title":"Knowl-Based Syst"},{"key":"1622_CR16","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.ins.2019.07.005","volume":"504","author":"W Gan","year":"2019","unstructured":"Gan W, Lin JCW, Chao HC, Fujita H, Yu PS (2019) Correlated utility-based pattern mining. Inf Sci 504:470\u2013486","journal-title":"Inf Sci"},{"key":"1622_CR17","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.knosys.2017.12.029","volume":"144","author":"U Yun","year":"2018","unstructured":"Yun U, Kim D, Yoon E, Fujita H (2018) Damped window based high average utility pattern mining over data streams. Knowl-Based Syst 144:188\u2013205","journal-title":"Knowl-Based Syst"},{"key":"1622_CR18","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.knosys.2017.10.016","volume":"139","author":"Y Djenouri","year":"2018","unstructured":"Djenouri Y, Belhadi A, Fournier-Viger P (2018) Extracting useful knowledge from event logs: a frequent itemset mining approach. Knowl-Based Syst 139:132\u2013148","journal-title":"Knowl-Based Syst"},{"key":"1622_CR19","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.knosys.2006.08.005","volume":"20","author":"J Dong","year":"2007","unstructured":"Dong J, Han M (2007) BitTableFI: an efficient mining frequent itemsets algorithm. Knowl-Based Syst 20:329\u2013335","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"1622_CR20","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s13042-014-0252-2","volume":"7","author":"B Vo","year":"2016","unstructured":"Vo B, Le T, Coenen F, Hong TP (2016) Mining frequent itemsets using the N-list and subsume concepts. Int J Mach Learn Cybern 7(2):253\u2013265","journal-title":"Int J Mach Learn Cybern"},{"key":"1622_CR21","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Li Y, Wang J, Wang J (2009) Frequent pattern mining with uncertain data. In: KDD, pp. 29-38","DOI":"10.1145\/1557019.1557030"},{"key":"1622_CR22","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.future.2016.09.007","volume":"68","author":"G Lee","year":"2017","unstructured":"Lee G, Yun U (2017) A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives. Futur Gener Comput Syst 68:89\u2013110","journal-title":"Futur Gener Comput Syst"},{"key":"1622_CR23","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.knosys.2015.08.018","volume":"90","author":"G Lee","year":"2015","unstructured":"Lee G, Yun U, Ryang H (2015) An uncertainty-based approach: frequent itemset mining from uncertain data with different item importance. Knowl-Based Syst 90:239\u2013256","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"1622_CR24","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1007\/s10489-015-0703-9","volume":"44","author":"CW Lin","year":"2016","unstructured":"Lin CW, Gan W, Fournier-Viger P, Hong TP, Tseng VS (2016a) Weighted frequent itemset mining over uncertain databases. Appl Intell 44(1):232\u2013250","journal-title":"Appl Intell"},{"key":"1622_CR25","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.knosys.2015.12.019","volume":"96","author":"JCW Lin","year":"2016","unstructured":"Lin JCW, Gan W, Fournier-Viger P, Hong TP, Tseng VS (2016b) Efficient algorithms for mining high-utility itemsets in uncertain databases. Knowl-Based Syst 96:171\u2013187","journal-title":"Knowl-Based Syst"},{"key":"1622_CR26","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.datak.2015.07.012","volume":"100","author":"YH Liu","year":"2015","unstructured":"Liu YH (2015) Mining time-interval univariate uncertain sequential patterns. Data Knowl Eng 100:54\u201377","journal-title":"Data Knowl Eng"},{"key":"1622_CR27","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.engappai.2015.05.003","volume":"44","author":"AM Palacios","year":"2015","unstructured":"Palacios AM, Mart\u00ednez A, S\u00e1nchez L, Couso I (2015) Sequential pattern mining applied to aeroengine condition monitoring with uncertain health data. Eng Appl Artif Intell 44:10\u201324","journal-title":"Eng Appl Artif Intell"},{"key":"1622_CR28","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.ins.2016.03.007","volume":"354","author":"AU Ahmed","year":"2016","unstructured":"Ahmed AU, Ahmed CF, Samiullah M, Adnan N, Leung CKS (2016) Mining interesting patterns from uncertain databases. Inf Sci 354:60\u201385","journal-title":"Inf Sci"},{"key":"1622_CR29","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.knosys.2016.04.016","volume":"104","author":"QH Duong","year":"2016","unstructured":"Duong QH, Liao B, Fournier-Viger P, Dam TL (2016) An efficient algorithm for mining the top-k high utility itemsets, using novel threshold raising and pruning strategies. Knowl-Based Syst 104:106\u2013122","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"1622_CR30","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1007\/s10618-016-0467-9","volume":"30","author":"F Petitjean","year":"2016","unstructured":"Petitjean F, Li T, Tatti N, Webb GI (2016) Skopus: mining top-k sequential patterns under leverage. Data Min Knowl Disc 30(5):1086\u20131111","journal-title":"Data Min Knowl Disc"},{"key":"1622_CR31","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2014.12.010","volume":"76","author":"H Ryang","year":"2015","unstructured":"Ryang H, Yun U (2015) Top-k high utility pattern mining with effective threshold raising strategies. Knowl-Based Syst 76:109\u2013126","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"1622_CR32","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/TKDE.2015.2458860","volume":"28","author":"V Tseng","year":"2016","unstructured":"Tseng V, Wu C, Fournier-Viger P, Yu PS (2016) Efficient algorithms for mining top-K high utility Itemsets. IEEE Trans Knowl Data Eng 28(1):54\u201367","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1622_CR33","unstructured":"Aggarwal CC, Han J (2014) Frequent pattern mining. Springer, ISBN 978-3-319-07820-5"},{"key":"1622_CR34","unstructured":"Agrawal R., Srikant R.: Fast algorithms for mining association rules. In: VLDB'94, 487\u2013499, 1994"},{"key":"1622_CR35","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1109\/TKDE.2005.166","volume":"17","author":"G Grahne","year":"2005","unstructured":"Grahne G, Zhu J (2005) Fast algorithms for frequent itemset mining using FP-trees. IEEE Trans Knowl Data Eng 17:1347\u20131362","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1622_CR36","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.knosys.2008.03.011","volume":"21","author":"W Song","year":"2008","unstructured":"Song W, Yang B, Xu Z (2008) Index-BitTableFI: an improved algorithm for mining frequent itemsets. Knowl-Based Syst 21:507\u2013513","journal-title":"Knowl-Based Syst"},{"key":"1622_CR37","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.asoc.2016.01.010","volume":"41","author":"ZH Deng","year":"2016","unstructured":"Deng ZH (2016) DiffNodesets: an efficient structure for fast mining frequent itemsets. Appl Soft Comput 41:214\u2013223","journal-title":"Appl Soft Comput"},{"issue":"13","key":"1622_CR38","doi-asserted-by":"crossref","first-page":"5424","DOI":"10.1016\/j.eswa.2015.03.004","volume":"42","author":"ZH Deng","year":"2015","unstructured":"Deng ZH, Lv SL (2015) PrePost+: an efficient N-lists-based algorithm for mining frequent itemsets via children-parent equivalence pruning. Expert Syst Appl 42(13):5424\u20135432","journal-title":"Expert Syst Appl"},{"key":"1622_CR39","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.knosys.2018.04.001","volume":"152","author":"H Fasihy","year":"2018","unstructured":"Fasihy H, Nadimi-Shahraki MH (2018) Incremental mining maximal frequent patterns from univariate uncertain data. Knowl-Based Syst 152:40\u201350","journal-title":"Knowl-Based Syst"},{"key":"1622_CR40","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.eswa.2016.12.023","volume":"73","author":"B Vo","year":"2017","unstructured":"Vo B, Pham S, Le T, Deng ZH (2017) A novel approach for mining maximal frequent patterns. Expert Syst Appl 73:178\u2013186","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1622_CR41","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1007\/s10489-015-0748-9","volume":"45","author":"TL Dam","year":"2016","unstructured":"Dam TL, Li K, Fournier-Viger P (2016) An efficient algorithm for mining top-rank-k frequent patterns. Appl Intell 45(1):96\u2013111","journal-title":"Appl Intell"},{"issue":"4","key":"1622_CR42","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1016\/j.eswa.2013.08.075","volume":"41","author":"ZH Deng","year":"2014","unstructured":"Deng ZH (2014) Fast mining top-rank-k frequent patterns by using node-lists. Expert Syst Appl 41(4):1763\u20131768","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1622_CR43","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.eswa.2014.07.045","volume":"42","author":"Q Huynh","year":"2015","unstructured":"Huynh Q, Le T, Vo B, Le B (2015) An efficient and effective algorithm for mining top-rank-k frequent patterns. Expert Syst Appl 42(1):156\u2013164","journal-title":"Expert Syst Appl"},{"issue":"2","key":"1622_CR44","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.3233\/JIFS-169128","volume":"32","author":"LTT Nguyen","year":"2017","unstructured":"Nguyen LTT, Trinh T, Nguyen NT, Vo B (2017) A method for mining top-rank-k frequent closed itemsets. J Intell Fuzzy Syst 32(2):1297\u20131305","journal-title":"J Intell Fuzzy Syst"},{"issue":"1","key":"1622_CR45","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10115-014-0732-4","volume":"43","author":"J Sahoo","year":"2015","unstructured":"Sahoo J, Das AK, Goswami A (2015) An effective ssociation rule mining scheme using a new generic basis. Knowl Inf Syst 43(1):127\u2013156","journal-title":"Knowl Inf Syst"},{"issue":"4","key":"1622_CR46","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1002\/int.21580","volume":"28","author":"ZH Deng","year":"2013","unstructured":"Deng ZH (2013) Mining top-rank-k erasable Itemsets by PID_lists. Int J Intell Syst 28(4):366\u2013379","journal-title":"Int J Intell Syst"},{"key":"1622_CR47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.engappai.2017.09.010","volume":"68","author":"T Le","year":"2018","unstructured":"Le T, Vo B, Baik SW (2018) Efficient algorithms for mining top-rank-k erasable patterns using pruning strategies and the subsume concept. Eng Appl Artif Intell 68:1\u20139","journal-title":"Eng Appl Artif Intell"},{"issue":"4","key":"1622_CR48","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1007\/s10489-017-0939-7","volume":"47","author":"S Dawar","year":"2017","unstructured":"Dawar S, Sharma V, Goyal V (2017) Mining top-k high-utility itemsets from a data stream under sliding window model. Appl Intell 47(4):1240\u20131255","journal-title":"Appl Intell"},{"key":"1622_CR49","doi-asserted-by":"crossref","unstructured":"Bui N, Vo B, Huynh VN, Lin CW, Nguyen LTT (2016) Mining closed high utility itemsets in uncertain databases. In: SoICT, pp. 7\u201314","DOI":"10.1145\/3011077.3011124"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01622-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-019-01622-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01622-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T01:14:34Z","timestamp":1611278074000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-019-01622-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,23]]},"references-count":49,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,5]]}},"alternative-id":["1622"],"URL":"https:\/\/doi.org\/10.1007\/s10489-019-01622-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2020,1,23]]},"assertion":[{"value":"23 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}