{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:04:55Z","timestamp":1754154295774,"version":"3.41.2"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"7-8","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"crossref","award":["2023NSFSC1420","2023NSFSC1420"],"award-info":[{"award-number":["2023NSFSC1420","2023NSFSC1420"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Longquanyi District Science and Technology Program of Chengdu, Sichuan","award":["2024LQRD0055","2024LQRD0055","2024LQRD0055"],"award-info":[{"award-number":["2024LQRD0055","2024LQRD0055","2024LQRD0055"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42371437","42371437"],"award-info":[{"award-number":["42371437","42371437"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Program of China","award":["2023YFG0130"],"award-info":[{"award-number":["2023YFG0130"]}]},{"name":"Sichuan Transfer Payment Application Program of China","award":["R22ZYZF0004"],"award-info":[{"award-number":["R22ZYZF0004"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s13042-025-02533-z","type":"journal-article","created":{"date-parts":[[2025,1,19]],"date-time":"2025-01-19T22:23:16Z","timestamp":1737325396000},"page":"4655-4672","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Three-way unsupervised anomaly detection of sequential patterns"],"prefix":"10.1007","volume":"16","author":[{"given":"Gong-Suo","family":"Chen","sequence":"first","affiliation":[]},{"given":"Tirapot","family":"Chandarasupsang","sequence":"additional","affiliation":[]},{"given":"Zhi-Heng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiang-Bing","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Wu","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Annop","family":"Tananchana","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Mu","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Min","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"issue":"3","key":"2533_CR1","doi-asserted-by":"publisher","first-page":"476","DOI":"10.5094\/APR.2015.053","volume":"6","author":"M Bobbia","year":"2015","unstructured":"Bobbia M, Misiti M, Misiti Y, Poggi JM, Portier B (2015) Spatial outlier detection in the pm10 monitoring network of normandy (france). Atmos Pollut Res 6(3):476\u2013483. https:\/\/doi.org\/10.5094\/APR.2015.053","journal-title":"Atmos Pollut Res"},{"key":"2533_CR2","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/j.neucom.2021.07.095","volume":"462","author":"J Carrasco","year":"2021","unstructured":"Carrasco J, Lopez D, Aguilera-Martos I (2021) Anomaly detection in predictive maintenance: a new evaluation framework for temporal unsupervised anomaly detection algorithms. Neurocomputing 462:440\u2013452. https:\/\/doi.org\/10.1016\/j.neucom.2021.07.095","journal-title":"Neurocomputing"},{"key":"2533_CR3","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ijar.2022.03.009","volume":"146","author":"W-B Chen","year":"2022","unstructured":"Chen W-B, Zhang Q-H, Dai Y-Y (2022) Sequential multi-class three-way decisions based on cost-sensitive learning. Int J Approx Reason 146:47\u201361. https:\/\/doi.org\/10.1016\/j.ijar.2022.03.009","journal-title":"Int J Approx Reason"},{"key":"2533_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3411832","volume":"4","author":"S Deldari","year":"2020","unstructured":"Deldari S, Smith DV, Sadri A, Salim F (2020) ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data. ACM Interact Mob Wearab Ubiquit Technol 4:1\u201324. https:\/\/doi.org\/10.1145\/3411832","journal-title":"ACM Interact Mob Wearab Ubiquit Technol"},{"key":"2533_CR5","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.knosys.2015.09.011","volume":"91","author":"H-L Dou","year":"2016","unstructured":"Dou H-L, Yang X-B, Song X-N, Yu H-L, Wu W-Z, Yang J-Y (2016) Decision-theoretic rough set: a multicost strategy. Knowl-Based Syst 91:71\u201383. https:\/\/doi.org\/10.1016\/j.knosys.2015.09.011","journal-title":"Knowl-Based Syst"},{"key":"2533_CR6","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.patcog.2016.03.028","volume":"58","author":"SM Erfani","year":"2016","unstructured":"Erfani SM, Rajasegarar S, Karunasekera S, Leckie C (2016) High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning. Pattern Recogn 58:121\u2013134. https:\/\/doi.org\/10.1016\/j.patcog.2016.03.028","journal-title":"Pattern Recogn"},{"key":"2533_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.130880","volume":"294","author":"Y Fang","year":"2024","unstructured":"Fang Y, Jia C, Wang X, Min F (2024) A fusion gas load prediction model with three-way residual error amendment. Energy 294:130880. https:\/\/doi.org\/10.1016\/j.energy.2024.130880","journal-title":"Energy"},{"key":"2533_CR8","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10462-004-4304-y","volume":"11","author":"VJ Hodge","year":"2004","unstructured":"Hodge VJ, Austin J (2004) A survey of outlier detection methodologies. Artif Intell Rev 11:85\u2013126. https:\/\/doi.org\/10.1007\/s10462-004-4304-y","journal-title":"Artif Intell Rev"},{"issue":"17\u201318","key":"2533_CR9","doi-asserted-by":"publisher","first-page":"7636","DOI":"10.1007\/s10489-024-05575-y","volume":"54","author":"X-H Huang","year":"2024","unstructured":"Huang X-H, Chen N-J, Deng Z-Y, Huang S-Q (2024) Multivariate time series anomaly detection via dynamic graph attention network and informer. Appl Intell 54(17\u201318):7636\u20137658. https:\/\/doi.org\/10.1007\/s10489-024-05575-y","journal-title":"Appl Intell"},{"issue":"4","key":"2533_CR10","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1145\/322033.322044","volume":"24","author":"DS Hirschberg","year":"1977","unstructured":"Hirschberg DS (1977) Algorithms for the longest common subsequence problem. J ACM 24(4):664\u2013675. https:\/\/doi.org\/10.1145\/322033.322044","journal-title":"J ACM"},{"key":"2533_CR11","doi-asserted-by":"publisher","first-page":"5881","DOI":"10.1007\/s10462-022-10316-z","volume":"382","author":"Z-H Jia","year":"2023","unstructured":"Jia Z-H, Qiao J-S (2023) New constructions of decision evaluation functions in three-way decision spaces based on uninorms. Artif Intell Rev 382:5881\u20135927. https:\/\/doi.org\/10.1007\/s10462-022-10316-z","journal-title":"Artif Intell Rev"},{"key":"2533_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105659","volume":"195","author":"P Karczmarek","year":"2020","unstructured":"Karczmarek P, Kiersztyn A, Pedrycz W, Al E (2020) K-means-based isolation forest. Knowl-Based Syst 195:105659. https:\/\/doi.org\/10.1016\/j.knosys.2020.105659","journal-title":"Knowl-Based Syst"},{"key":"2533_CR13","doi-asserted-by":"publisher","unstructured":"Keogh E, Lonardi S, Chiu BY-c (2002) Finding surprising patterns in a time series database in linear time and space. In: The eighth ACM SIGKDD international conference on knowledge discovery and data mining, pp. 550\u2013556 . https:\/\/doi.org\/10.1145\/775047.775128","DOI":"10.1145\/775047.775128"},{"key":"2533_CR14","doi-asserted-by":"publisher","unstructured":"Keogh E, Lin J, Fu A (2005) HOT-SAX: efficiently finding the most unusual time series subsequence. In: The fifth IEEE international conference on data mining, pp. 226\u2013233 . https:\/\/doi.org\/10.1109\/ICDM.2005.79","DOI":"10.1109\/ICDM.2005.79"},{"issue":"2","key":"2533_CR15","doi-asserted-by":"publisher","first-page":"377","DOI":"10.24507\/ijicic.18.02.377","volume":"18","author":"Q-Y Lv","year":"2022","unstructured":"Lv Q-Y, Dong M-G (2022) Active learning of three-way decision based on neighborhood entropy. Int J Innov Comput Inf Control 18(2):377\u2013393. https:\/\/doi.org\/10.24507\/ijicic.18.02.377","journal-title":"Int J Innov Comput Inf Control"},{"key":"2533_CR16","doi-asserted-by":"publisher","unstructured":"Liu Z, Huang X, Zhang J-Y, Hao Z-F, Sun L, Peng H (2024) Multivariate time-series anomaly detection based on enhancing graph attention networks with topological analysis. In: The thirty-third ACM international conference on information and knowledge management, pp 1555\u20131564 . https:\/\/doi.org\/10.1145\/3627673.3679614","DOI":"10.1145\/3627673.3679614"},{"key":"2533_CR17","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.ijar.2018.12.008","volume":"105","author":"W-W Li","year":"2019","unstructured":"Li W-W, Jia X-Y, Wang L, Zhou B (2019) Multi-objective attribute reduction in three-way decision-theoretic rough set model. Int J Approx Reason 105:327\u2013341. https:\/\/doi.org\/10.1016\/j.ijar.2018.12.008","journal-title":"Int J Approx Reason"},{"issue":"2","key":"2533_CR18","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10618-007-0064-z","volume":"15","author":"J Lin","year":"2007","unstructured":"Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing SAX: a novel symbolic representation of time series. Data Min Knowl Discov 15(2):107\u2013144. https:\/\/doi.org\/10.1007\/s10618-007-0064-z","journal-title":"Data Min Knowl Discov"},{"key":"2533_CR19","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/j.ins.2018.12.017","volume":"507","author":"D-C Liang","year":"2020","unstructured":"Liang D-C, Wang M-W, Xu Z-S, Liu D (2020) Risk appetite dual hesitant fuzzy three-way decisions with todim. Inf Sci 507:585\u2013605. https:\/\/doi.org\/10.1016\/j.ins.2018.12.017","journal-title":"Inf Sci"},{"key":"2533_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105243","volume":"191","author":"D Liu","year":"2020","unstructured":"Liu D, Ye X-Q (2020) A matrix factorization based dynamic granularity recommendation with three-way decisions. Knowl-Based Syst 191:105243. https:\/\/doi.org\/10.1016\/j.knosys.2019.105243","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"2533_CR21","doi-asserted-by":"publisher","first-page":"3318","DOI":"10.3390\/s23063318","volume":"23","author":"DT Lan","year":"2023","unstructured":"Lan DT, Yoon S (2023) Trajectory clustering-based anomaly detection in indoor human movement. Sensors (Basel, Switzerland) 23(6):3318. https:\/\/doi.org\/10.3390\/s23063318","journal-title":"Sensors (Basel, Switzerland)"},{"key":"2533_CR22","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.ijar.2022.10.014","volume":"152","author":"G-M Lang","year":"2023","unstructured":"Lang G-M, Yao Y-Y (2023) Formal concept analysis perspectives on three-way conflict analysis. Int J Approx Reason 152:160\u2013182. https:\/\/doi.org\/10.1016\/j.ijar.2022.10.014","journal-title":"Int J Approx Reason"},{"issue":"4","key":"2533_CR23","doi-asserted-by":"publisher","first-page":"161","DOI":"10.2514\/1.I010394","volume":"13","author":"I Melnyk","year":"2016","unstructured":"Melnyk I, Banerjee A, Oza N (2016) Vector autoregressive model-based anomaly detection in aviation systems. J Aerosp Inf Syst 13(4):161\u2013173. https:\/\/doi.org\/10.2514\/1.I010394","journal-title":"J Aerosp Inf Syst"},{"issue":"2","key":"2533_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107443","volume":"108","author":"S Maleki","year":"2021","unstructured":"Maleki S, Maleki S, Jennings NR (2021) Unsupervised anomaly detection with lstm autoencoders using statistical data-filtering. Appl Soft Comput 108(2):107443. https:\/\/doi.org\/10.1016\/j.asoc.2021.107443","journal-title":"Appl Soft Comput"},{"key":"2533_CR25","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.ins.2015.07.007","volume":"326","author":"E Menahem","year":"2016","unstructured":"Menahem E, Schclar A, Rokach L, Elovici Y (2016) XML-AD: detecting anomalous patterns in xml documents. Inf Sci 326:71\u201388. https:\/\/doi.org\/10.1016\/j.ins.2015.07.007","journal-title":"Inf Sci"},{"issue":"1","key":"2533_CR26","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1613\/jair.374","volume":"7","author":"CG Nevill-Manning","year":"1997","unstructured":"Nevill-Manning CG, Witten IH (1997) Identifying hierarchical structure in sequences: a linear-time algorithm. J Artif Intell Res 7(1):67\u201382. https:\/\/doi.org\/10.1613\/jair.374","journal-title":"J Artif Intell Res"},{"key":"2533_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123439","volume":"248","author":"C Nixon","year":"2024","unstructured":"Nixon C, Sedky M, Champion J, Hassan M (2024) SALAD: a split active learning based unsupervised network data stream anomaly detection method using autoencoders. Expert Syst Appl 248:123439. https:\/\/doi.org\/10.1016\/j.eswa.2024.123439","journal-title":"Expert Syst Appl"},{"key":"2533_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.121316","volume":"686","author":"J Qian","year":"2024","unstructured":"Qian J, Zheng M-C, Yu Y, Zhou C-P, Miao D-Q (2024) A dynamic anonymization privacy-preserving model based on hierarchical sequential three-way decisions. Inf Sci 686:121316. https:\/\/doi.org\/10.1016\/j.ins.2024.121316","journal-title":"Inf Sci"},{"key":"2533_CR29","doi-asserted-by":"publisher","unstructured":"Senin P, Lin J, Wang X (2015) Time series anomaly discovery with grammar-based compression. In: International conference on extending database technology, pp 481\u2013492 . https:\/\/doi.org\/10.5441\/002\/edbt.2015.42","DOI":"10.5441\/002\/edbt.2015.42"},{"issue":"4","key":"2533_CR30","doi-asserted-by":"publisher","first-page":"144","DOI":"10.11896\/jsjkx.210600045","volume":"49","author":"S-P Shen","year":"2022","unstructured":"Shen S-P, Ma H-J, Zhang Z-H, Zhou X-B, Zhu C-M, Wen Z-C (2022) Three-way drift detection for state transition pattern on multivariate time series (Chinese). Comput Sci 49(4):144\u2013151. https:\/\/doi.org\/10.11896\/jsjkx.210600045","journal-title":"Comput Sci"},{"issue":"4","key":"2533_CR31","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1071\/WF17093","volume":"27","author":"MM Valero","year":"2021","unstructured":"Valero MM, Rios O, Planas E (2021) Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors. Int J Wildland Fire 27(4):241\u2013256. https:\/\/doi.org\/10.1071\/WF17093","journal-title":"Int J Wildland Fire"},{"key":"2533_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104379","volume":"104","author":"P Wu","year":"2021","unstructured":"Wu P, Harris CA, Salavasidis G, Lorenzo-Lopez A (2021) Unsupervised anomaly detection for underwater gliders using generative adversarial networks. Eng Appl Artif Intell 104:104379. https:\/\/doi.org\/10.1016\/j.engappai.2021.104379","journal-title":"Eng Appl Artif Intell"},{"key":"2533_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110762","volume":"276","author":"Z Wang","year":"2023","unstructured":"Wang Z, Shi C-J, Wei L, Yao Y-Y (2023) Tri-granularity attribute reduction of three-way concept lattices. Knowl-Based Syst 276:110762. https:\/\/doi.org\/10.1016\/j.knosys.2023.110762","journal-title":"Knowl-Based Syst"},{"key":"2533_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2022.109188","volume":"203","author":"J Wu","year":"2023","unstructured":"Wu J, Yang F, Hu W (2023) Unsupervised anomalous sound detection for industrial monitoring based on arcface classifier and gaussian mixture model. Appl Acoust 203:109188. https:\/\/doi.org\/10.1016\/j.apacoust.2022.109188","journal-title":"Appl Acoust"},{"key":"2533_CR35","doi-asserted-by":"publisher","unstructured":"Wang C-S, Zhuang Z-R, Qi Q, Wang J-Y, Wang X-Y, Sun H-F, Liao J-X (2023) Drift doesn\u2019t Matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection. In: The thirty-seventh conference on neural information processing systems, pp 10758\u201310774 . https:\/\/doi.org\/10.5555\/3666122.3666595","DOI":"10.5555\/3666122.3666595"},{"issue":"1","key":"2533_CR36","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/TFUZZ.2014.2360548","volume":"23","author":"J-T Yao","year":"2014","unstructured":"Yao J-T, Azam N (2014) Web-based medical decision support systems for three-way medical decision making with game-theoretic rough sets. IEEE Trans Fuzzy Syst 23(1):3\u201315. https:\/\/doi.org\/10.1109\/TFUZZ.2014.2360548","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"2533_CR37","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.ins.2009.09.021","volume":"180","author":"YY Yao","year":"2010","unstructured":"Yao YY (2010) Three-way decisions with probabilistic rough sets. Inf Sci 180(3):341\u2013353. https:\/\/doi.org\/10.1016\/j.ins.2009.09.021","journal-title":"Inf Sci"},{"key":"2533_CR38","doi-asserted-by":"publisher","first-page":"6298","DOI":"10.1007\/s10489-020-02142-z","volume":"51","author":"YY Yao","year":"2021","unstructured":"Yao YY (2021) The geometry of three-way decision. Appl Intell 51:6298\u20136325. https:\/\/doi.org\/10.1007\/s10489-020-02142-z","journal-title":"Appl Intell"},{"key":"2533_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114454","volume":"168","author":"X-Q Ye","year":"2021","unstructured":"Ye X-Q, Liu D (2021) An interpretable sequential three-way recommendation based on collaborative topic regression. Expert Syst Appl 168:114454. https:\/\/doi.org\/10.1016\/j.eswa.2020.114454","journal-title":"Expert Syst Appl"},{"key":"2533_CR40","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.ins.2020.05.060","volume":"538","author":"X Yang","year":"2020","unstructured":"Yang X, Li T-R, Liu D, Fujita H (2020) A multilevel neighborhood sequential decision approach of three-way granular computing. Inf Sci 538:119\u2013141. https:\/\/doi.org\/10.1016\/j.ins.2020.05.060","journal-title":"Inf Sci"},{"key":"2533_CR41","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.ijepes.2018.03.025","volume":"101","author":"S-C Yip","year":"2018","unstructured":"Yip S-C, Tan W-N, Tan C, Gan M-T, Wong K (2018) An anomaly detection framework for identifying energy theft and defective meters in smart grids. Int J Electr Power Energy Syst 101:189\u2013203. https:\/\/doi.org\/10.1016\/j.ijepes.2018.03.025","journal-title":"Int J Electr Power Energy Syst"},{"key":"2533_CR42","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1016\/j.ins.2018.03.009","volume":"507","author":"H Yu","year":"2020","unstructured":"Yu H, Wang X-C, Wang G-Y, Zeng X-H (2020) An active three-way clustering method via low-rank matrices for multi-view data. Inf Sci 507:823\u2013839. https:\/\/doi.org\/10.1016\/j.ins.2018.03.009","journal-title":"Inf Sci"},{"key":"2533_CR43","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.cherd.2020.12.018","volume":"167","author":"A Zeberli","year":"2021","unstructured":"Zeberli A, Badr S, Siegmund C, Mattern M, Sugiyama H (2021) Data-driven anomaly detection and diagnostics for changeover processes in biopharmaceutical drug product manufacturing. Chem Eng Res Des 167:53\u201362. https:\/\/doi.org\/10.1016\/j.cherd.2020.12.018","journal-title":"Chem Eng Res Des"},{"key":"2533_CR44","doi-asserted-by":"publisher","unstructured":"Zhou Q-H, Chen J-M, Liu H-Y, He S-B, Meng W-C (2023) Detecting multivariate time series anomalies with zero known label. In: The thirty-seventh AAAI conference on artificial intelligence, vol 37, pp 4963\u20134971 . https:\/\/doi.org\/10.1609\/aaai.v37i4.25623","DOI":"10.1609\/aaai.v37i4.25623"},{"key":"2533_CR45","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.1007\/s12559-021-09871-4","volume":"14","author":"Z-H Zhang","year":"2022","unstructured":"Zhang Z-H, Min F, Chen G-S, Shen S-P, Wen Z-C, Zhou X-B (2022) Tri-partition state alphabet-based sequential pattern for multivariate time series. Cogn Comput 14:1881\u20131899. https:\/\/doi.org\/10.1007\/s12559-021-09871-4","journal-title":"Cogn Comput"},{"issue":"4","key":"2533_CR46","doi-asserted-by":"publisher","first-page":"4981","DOI":"10.1109\/TPAMI.2022.3198411","volume":"45","author":"N Zhang","year":"2022","unstructured":"Zhang N, Sun S-L (2022) Multiview unsupervised shapelet learning for multivariate time series clustering. IEEE Trans Pattern Anal Mach Intell 45(4):4981\u20134996. https:\/\/doi.org\/10.1109\/TPAMI.2022.3198411","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02533-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02533-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02533-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T03:37:04Z","timestamp":1753328224000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02533-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,20]]},"references-count":46,"journal-issue":{"issue":"7-8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["2533"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02533-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,1,20]]},"assertion":[{"value":"25 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2025","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 no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}