{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T05:04:43Z","timestamp":1773810283485,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T00:00:00Z","timestamp":1632096000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T00:00:00Z","timestamp":1632096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s10489-021-02505-0","type":"journal-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T22:03:16Z","timestamp":1632175396000},"page":"7136-7157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Mining interesting sequences with low average cost and high average utility"],"prefix":"10.1007","volume":"52","author":[{"given":"Tin","family":"Truong","sequence":"first","affiliation":[]},{"given":"Hai","family":"Duong","sequence":"additional","affiliation":[]},{"given":"Bac","family":"Le","sequence":"additional","affiliation":[]},{"given":"Philippe","family":"Fournier-Viger","sequence":"additional","affiliation":[]},{"given":"Unil","family":"Yun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,20]]},"reference":[{"key":"2505_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal R, Srikant R (1995) Mining sequential patterns. In Proceedings of the Eleventh International Conference on Data Engineering, pp.3\u201314","DOI":"10.1109\/ICDE.1995.380415"},{"key":"2505_CR2","doi-asserted-by":"crossref","unstructured":"Fournier-Viger P, Gomariz A, Campos M (2014) Fast vertical mining of sequential patterns using co-occurrence information. In Proceedings of 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD \u20182014, pp.40\u201352","DOI":"10.1007\/978-3-319-06608-0_4"},{"issue":"12","key":"2505_CR3","doi-asserted-by":"publisher","first-page":"4348","DOI":"10.1007\/s10489-019-01492-7","volume":"49","author":"R Wu","year":"2019","unstructured":"Wu R, Li Q, Chen X (2019) Mining contrast sequential pattern based on subsequence time distribution variation with discreteness constraints. Appl Intell 49(12):4348\u20134360","journal-title":"Appl Intell"},{"key":"2505_CR4","doi-asserted-by":"crossref","unstructured":"Ahmed CF, Tanbeer SK, Jeong BS (2010) Mining high utility web access sequences in dynamic web log data. In Proceedings of 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel\/Distributed Computing, SNPD2010, pp.76\u201381","DOI":"10.1109\/SNPD.2010.21"},{"key":"2505_CR5","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.eswa.2016.03.001","volume":"57","author":"H Ryang","year":"2016","unstructured":"Ryang H, Yun U (2016) High utility pattern mining over data streams with sliding window technique. Expert Syst Appl 57:214\u2013231","journal-title":"Expert Syst Appl"},{"issue":"8","key":"2505_CR6","doi-asserted-by":"publisher","first-page":"1772","DOI":"10.1109\/TKDE.2012.59","volume":"25","author":"VS Tseng","year":"2013","unstructured":"Tseng VS, Shie BE, Wu CW, Yu PS (2013) Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans Knowl Data Eng 25(8):1772\u20131786","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2505_CR7","doi-asserted-by":"crossref","unstructured":"Zihayat M, Davoudi H, An A (2017) Top-k utility-based gene regulation sequential pattern discovery. In Proceedings of 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, pp.266\u2013273","DOI":"10.1109\/BIBM.2016.7822529"},{"key":"2505_CR8","doi-asserted-by":"crossref","unstructured":"Dalmas B, Fournier-Viger P, Norre S (2017) TWINCLE: a constrained sequential rule mining algorithm for event logs. In Proceedings of 9th International KES Conference (IDT-KES 2017), pp.205\u2013214","DOI":"10.1016\/j.procs.2017.08.069"},{"key":"2505_CR9","doi-asserted-by":"publisher","first-page":"106596","DOI":"10.1016\/j.knosys.2020.106596","volume":"212","author":"Y Baek","year":"2021","unstructured":"Baek Y, Yun U, Kim H, Kim J, Vo B, Truong T (2021) Approximate high utility itemset mining in noisy environments. Knowledge-Based Syst 212:106596","journal-title":"Knowledge-Based Syst"},{"key":"2505_CR10","unstructured":"Chan R, Yang Q, Shen Y-D (2003) Minging high utility itemsets. In Proceedings of IEEE International Conference on Data Mining, pp.19\u201326"},{"key":"2505_CR11","doi-asserted-by":"crossref","unstructured":"Fournier-Viger P, Lin JC-W, Truong T, Nkambou R (2019) A survey of high utility Itemset mining. In High-Utility Pattern Mining: Theory, Algorithms and Applications; Fournier-Viger, Philippe; Jerry Chun-Wei., Lin; Nikambou, Roger; Vo, Bay; Tseng, Vincent S, Springer International Publishing. pp.1\u201344","DOI":"10.1007\/978-3-030-04921-8_1"},{"key":"2505_CR12","doi-asserted-by":"crossref","unstructured":"Yin J, Zheng Z, Cao L (2012) USpan: An efficient algorithm for mining high utility sequential patterns. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.660\u2013668","DOI":"10.1145\/2339530.2339636"},{"issue":"2","key":"2505_CR13","first-page":"1","volume":"1904","author":"W Gan","year":"2019","unstructured":"Gan W, Lin JC-W, Zhang J, Fournier-Viger P, Chao H, Yu PS (2019) Fast utility mining on complex sequences. CoRR 1904(2):1\u201315","journal-title":"CoRR"},{"issue":"1","key":"2505_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.engappai.2019.05.010","volume":"85","author":"T Truong","year":"2019","unstructured":"Truong T, Duong H, Le B, Fournier-Viger P (2019) FMaxCloHUSM: An efficient algorithm for mining frequent closed and maximal high utility sequences. Eng Appl Artif Intell 85(1):1\u201320","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"2505_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2015.06.014","volume":"89","author":"J Zhang","year":"2015","unstructured":"Zhang J, Wang Y, Yang D (2015) CCSpan: mining closed contiguous sequential patterns. Knowledge-Based Syst 89(1):1\u201313","journal-title":"Knowledge-Based Syst"},{"issue":"5","key":"2505_CR16","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1109\/TCBB.2015.2495132","volume":"13","author":"J Zhang","year":"2016","unstructured":"Zhang J, Wang Y, Zhang C, Shi Y (2016) Mining contiguous sequential generators in biological sequences. IEEE\/ACM Trans Comput Biol Bioinforma 13(5):855\u2013867","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma"},{"key":"2505_CR17","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.ins.2021.01.060","volume":"568","author":"T Truong","year":"2021","unstructured":"Truong T, Duong H, Le B, Fournier-Viger P, Yun U, Fujita H (2021) Efficient algorithms for mining frequent high utility sequences with constraints. Inf Sci (Ny) 568:239\u2013264","journal-title":"Inf Sci (Ny)"},{"key":"2505_CR18","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.ins.2019.05.006","volume":"495","author":"LTT Nguyen","year":"2019","unstructured":"Nguyen LTT, Vu VV, Lam MTH, Duong TTM, Manh LT, Nguyen TTT et al (2019) An efficient method for mining high utility closed itemsets. Inf Sci (Ny) 495:78\u201399","journal-title":"Inf Sci (Ny)"},{"issue":"7","key":"2505_CR19","doi-asserted-by":"publisher","first-page":"8259","DOI":"10.1016\/j.eswa.2011.01.006","volume":"38","author":"T-P Hong","year":"2011","unstructured":"Hong T-P, Lee CH, Wang SL (2011) Effective utility mining with the measure of average utility. Expert Syst Appl 38(7):8259\u20138265","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2505_CR20","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.future.2016.10.027","volume":"68","author":"U Yun","year":"2017","unstructured":"Yun U, Kim D (2017) Mining of high average-utility itemsets using novel list structure and pruning strategy. Futur Gener Comput Syst 68(1):346\u2013360","journal-title":"Futur Gener Comput Syst"},{"issue":"2","key":"2505_CR21","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1109\/TKDE.2018.2833478","volume":"31","author":"T Truong","year":"2018","unstructured":"Truong T, Duong H, Le B, Fournier-Viger P (2018) Efficient vertical Mining of High Average-Utility Itemsets Based on novel upper-bounds. IEEE Trans Knowl Data Eng 31(2):301\u2013314","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"2505_CR22","doi-asserted-by":"publisher","first-page":"104847","DOI":"10.1016\/j.knosys.2019.07.018","volume":"183","author":"T Truong","year":"2019","unstructured":"Truong T, Duong H, Le B, Fournier-Viger P, Yun U (2019) Efficient high average-utility itemset mining using novel vertical weak upper-bounds. Knowledge-Based Syst. 183(1):104847","journal-title":"Knowledge-Based Syst."},{"issue":"1","key":"2505_CR23","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.ins.2019.11.018","volume":"515","author":"T Truong","year":"2020","unstructured":"Truong T, Duong H, Le B, Fournier-Viger P (2020) EHAUSM: An efficient algorithm for high average utility sequence mining. Inf Sci (Ny) 515(1):302\u2013323","journal-title":"Inf Sci (Ny)"},{"key":"2505_CR24","doi-asserted-by":"publisher","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. Knowledge-Based Syst 144:188\u2013205","journal-title":"Knowledge-Based Syst"},{"key":"2505_CR25","doi-asserted-by":"crossref","unstructured":"Zida S, Fournier-Viger P, Lin JC-W, Wu C-W, Tseng VS (2015) EFIM: a highly efficient algorithm for high-utility itemset mining. In Proceedings of Mexican International Conference on Artificial Intelligence (MICAI 2015), pp.530\u2013546","DOI":"10.1007\/978-3-319-27060-9_44"},{"key":"2505_CR26","doi-asserted-by":"crossref","unstructured":"Fournier-viger P, Zhang Y, Lin JC, Fujita H, Koh YS (2019) Mining local and peak high utility itemsets. Inf Sci (Ny). 481 344\u2013367","DOI":"10.1016\/j.ins.2018.12.070"},{"key":"2505_CR27","doi-asserted-by":"crossref","unstructured":"Kim H, Yun U, Baek Y, Kim J, Vo B, Yoon E, et al. (2021) Efficient list based mining of high average utility patterns with maximum average pruning strategies. Inf Sci (Ny). 543 85\u2013105","DOI":"10.1016\/j.ins.2020.07.043"},{"issue":"5","key":"2505_CR28","doi-asserted-by":"publisher","first-page":"676","DOI":"10.4218\/etrij.10.1510.0066","volume":"32","author":"CF Ahmed","year":"2010","unstructured":"Ahmed CF, Tanbeer SK, Jeong BS (2010) A novel approach for mining high-utility sequential patterns in sequence databases. ETRI 32(5):676\u2013686","journal-title":"ETRI"},{"issue":"2","key":"2505_CR29","first-page":"65","volume":"4","author":"T Truong","year":"2020","unstructured":"Truong T, Tran A, Duong H, Le B, Fournier-Viger P (2020) EHUSM\u00a0: mining high utility sequences with a pessimistic utility model. Data Sci Pattern Recognit 4(2):65\u201383","journal-title":"Data Sci Pattern Recognit"},{"issue":"10","key":"2505_CR30","doi-asserted-by":"publisher","first-page":"2645","DOI":"10.1109\/TKDE.2015.2420557","volume":"27","author":"OK Alkan","year":"2015","unstructured":"Alkan OK, Karagoz P (2015) CRoM and HuspExt: improving efficiency of high utility sequential pattern extraction. IEEE Trans Knowl Data Eng 27(10):2645\u20132657","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"2505_CR31","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/s10115-015-0914-8","volume":"49","author":"JZ Wang","year":"2016","unstructured":"Wang JZ, Huang JL, Chen YC (2016) On efficiently mining high utility sequential patterns. Knowl Inf Syst 49(2):597\u2013627","journal-title":"Knowl Inf Syst"},{"key":"2505_CR32","doi-asserted-by":"publisher","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. Knowledge-Based Syst 143:30\u201341","journal-title":"Knowledge-Based Syst"},{"key":"2505_CR33","doi-asserted-by":"crossref","unstructured":"Gan W, Lin JC, Zhang J, Chao H, Fujita H, Yu PS (2020) ProUM\u00a0: projection-based utility mining on sequence data. Inf Sci (Ny). 513 222\u2013240","DOI":"10.1016\/j.ins.2019.10.033"},{"key":"2505_CR34","doi-asserted-by":"crossref","unstructured":"Gan W, Lin JC, Chao H, Fujita H, Yu PS (2019) Correlated utility-based pattern mining. Inf Sci (Ny). 504 470\u2013486","DOI":"10.1016\/j.ins.2019.07.005"},{"key":"2505_CR35","doi-asserted-by":"crossref","unstructured":"Yin J, Zheng Z, Cao L, Song Y, Wei W (2013) Efficiently mining top-K high utility sequential patterns. In Proceedings of 2013 IEEE 13th International Conference on Data Mining (ICDM), pp.1259\u20131264","DOI":"10.1109\/ICDM.2013.148"},{"key":"2505_CR36","doi-asserted-by":"crossref","unstructured":"Truong T, Fournier-Viger P (2019) A survey of high utility sequential pattern mining. In P. Fournier-Viger, J. C.-W. Lin, R. Nkambou, V. Bay, & V. S. Tseng, High-utility pattern mining: theory, algorithms and applications, pp.97\u2013129","DOI":"10.1007\/978-3-030-04921-8_4"},{"issue":"1","key":"2505_CR37","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.protcy.2012.10.053","volume":"6","author":"M Thilagu","year":"2012","unstructured":"Thilagu M, Nadarajan R (2012) Efficiently mining of effective web traversal patterns with average utility. Procedia Technol 6(1):444\u2013451","journal-title":"Procedia Technol"},{"issue":"3","key":"2505_CR38","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1007\/s10115-019-01385-8","volume":"62","author":"JC-W Lin","year":"2020","unstructured":"Lin JC-W, Li T, Pirouz M, Zhang J, Fournier-Viger P (2020) High average-utility sequential pattern mining based on uncertain databases. Knowl Inf Syst 62(3):1199\u20131228","journal-title":"Knowl Inf Syst"},{"key":"2505_CR39","doi-asserted-by":"crossref","unstructured":"Fournier-Viger P, Li J, Lin JC-W, Truong T (2019) Discovering and visualizing efficient patterns in cost\/utility sequences. In Proceedings of International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2019), LNCS 11708, pp.73\u201388","DOI":"10.1007\/978-3-030-27520-4_6"},{"key":"2505_CR40","first-page":"105241","volume":"191","author":"P Fournier-Viger","year":"2020","unstructured":"Fournier-Viger P, Li J, Lin JC-W, Truong T, Kiran RU (2020) Mining cost-effective patterns in event logs Knowledge-Based Syst 191:105241","journal-title":"Mining cost-effective patterns in event logs Knowledge-Based Syst"},{"issue":"1","key":"2505_CR41","first-page":"3389","volume":"15","author":"P Fournier-Viger","year":"2014","unstructured":"Fournier-Viger P, Lin JC-W, Gomaris A, Gueniche T, Soltani A, Deng Z et al (2014) SPMF: a Java open-source pattern mining library version 2. Mach Learn Res 15(1):3389\u20133393","journal-title":"Mach Learn Res"},{"key":"2505_CR42","first-page":"2526","volume-title":"Mining high average-utility itemsets","author":"T-P Hong","year":"2009","unstructured":"Hong T-P, Lee CH, Wang SL (2009) Mining high average-utility itemsets. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp 2526\u20132530"},{"key":"2505_CR43","unstructured":"Mehrnoosh V, Luca O, Davide A, Mathias F, Matthias R (2015) A learning analytics approach to correlate the academic achievements of students with interaction data from an educational simulator. In Lecture Notes in Computer Science, pp.613\u2013616"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02505-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02505-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02505-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T09:18:05Z","timestamp":1651742285000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02505-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,20]]},"references-count":43,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["2505"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02505-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,20]]},"assertion":[{"value":"4 May 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}