{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:52:12Z","timestamp":1758311532926,"version":"3.44.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T00:00:00Z","timestamp":1751673600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T00:00:00Z","timestamp":1751673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100020735","name":"Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space","doi-asserted-by":"publisher","award":["T3142411SN"],"award-info":[{"award-number":["T3142411SN"]}],"id":[{"id":"10.13039\/501100020735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10489-025-06752-3","type":"journal-article","created":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T04:56:59Z","timestamp":1751691419000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive variable-length subsequence pattern extraction in time series"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6612-1924","authenticated-orcid":false,"given":"Ke","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangyong","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiantian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congmin","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,5]]},"reference":[{"key":"6752_CR1","unstructured":"Jensen SK, Pedersen TB, Thomsen C (2022) Time series management systems: A 2022 survey. DATA SERIES MANAGEMENT AND ANALYTICS, 81. Ass Comput Mach"},{"key":"6752_CR2","doi-asserted-by":"crossref","unstructured":"Zhang Q, Wu J, Zhang P, Long G, Zhang C (2018) Salient subsequence learning for time series clustering. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 41(9):2193\u20132207. IEEE","DOI":"10.1109\/TPAMI.2018.2847699"},{"key":"6752_CR3","doi-asserted-by":"crossref","unstructured":"Ezugwu AE, Ikotun AM, Oyelade OO, Abualigah L, Agushaka JO, Eke CI, Akinyelu AA (2022) A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Eng Appl Artif Intell 110(104743). Elsevier","DOI":"10.1016\/j.engappai.2022.104743"},{"key":"6752_CR4","doi-asserted-by":"crossref","unstructured":"Aghabozorgi S, Shirkhorshidi AS, Wah TY (2015) Time-series clustering\u2013a decade review. Inf Syst 53, 16\u201338. Elsevier","DOI":"10.1016\/j.is.2015.04.007"},{"key":"6752_CR5","doi-asserted-by":"crossref","unstructured":"Zolhavarieh S, Aghabozorgi S, Teh YW, et al (2014) A review of subsequence time series clustering. Sci World J 2014(312521). Hindawi","DOI":"10.1155\/2014\/312521"},{"key":"6752_CR6","unstructured":"PhysioBank ATM. https:\/\/archive.physionet.org\/cgi-bin\/atm\/ATM"},{"key":"6752_CR7","doi-asserted-by":"crossref","unstructured":"Duan J, Guo L (2022) Variable-length subsequence clustering in time series. IEEE Trans Knowl Data Eng 34(2):983\u2013995. IEEE","DOI":"10.1109\/TKDE.2020.2986965"},{"key":"6752_CR8","unstructured":"Das G, Lin K-I, Mannila H, Renganathan G, Smyth P (1998) Rule discovery from time series. ACM Sigkdd Conf Knowl Disc Data Mining 98:16\u201322. Citeseer"},{"key":"6752_CR9","unstructured":"Bezdek JC (2013) Pattern Recognition with Fuzzy Objective Function Algorithms,. Springer Science & Business Media"},{"key":"6752_CR10","doi-asserted-by":"crossref","unstructured":"Cherif A, Cardot H, Bon\u00e9 R (2011) Som time series clustering and prediction with recurrent neural networks. Neurocomputing 74(11):1936\u20131944. Elsevier","DOI":"10.1016\/j.neucom.2010.11.026"},{"issue":"4","key":"6752_CR11","doi-asserted-by":"publisher","first-page":"3433","DOI":"10.1109\/TKDE.2022.3155450","volume":"35","author":"F Nie","year":"2023","unstructured":"Nie F, Li Z, Wang R, Li X (2023) An effective and efficient algorithm for k-means clustering with new formulation. IEEE Trans Knowl Data Eng 35(4):3433\u20133443","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6752_CR12","doi-asserted-by":"crossref","unstructured":"Keogh E, Lin J (2005) Clustering of time-series subsequences is meaningless: implications for previous and future research. Knowl Inf Syst 8(2):154\u2013177. Springer","DOI":"10.1007\/s10115-004-0172-7"},{"key":"6752_CR13","doi-asserted-by":"crossref","unstructured":"Mueen A (2014) Time series motif discovery: dimensions and applications. WILEY Interdiscip Rev-Data Mining Knowl Disc 4(2):152\u2013159. Wiley Online Library","DOI":"10.1002\/widm.1119"},{"key":"6752_CR14","doi-asserted-by":"crossref","unstructured":"Ramanujam E, Padmavathi S (2019) Genetic time series motif discovery for time series classification. Int J Biomed Eng Technol 31(1):47\u201363. Inderscience Publishers (IEL)","DOI":"10.1504\/IJBET.2019.101051"},{"key":"6752_CR15","doi-asserted-by":"crossref","unstructured":"Siddiquee MA, Akhavan Z, Mueen A (2019) Seismo: Semi-supervised time series motif discovery for seismic signal detection. Proceed 28th ACM Int Conf Inf Knowl Manage, 99\u2013108. Assoc Comp Machinery; ACM SIGIR; ACM SIGWEB","DOI":"10.1145\/3357384.3357931"},{"issue":"2","key":"6752_CR16","first-page":"414","volume":"39","author":"Y Zhu","year":"2019","unstructured":"Zhu Y, Zhu X, Wang J (2019) Time series motif discovery algorithm based on subsequence full join and maximum clique. J Comput Appl 39(2):414\u2013420","journal-title":"J Comput Appl"},{"key":"6752_CR17","doi-asserted-by":"crossref","unstructured":"Cartwright E, Crane M, Ruskin HJ (2022) Side-length-independent motif (slim): Motif discovery and volatility analysis in time series\u2014sax, mdl and the matrix profile. FORECASTING 4(1):219\u2013237. MDPI","DOI":"10.3390\/forecast4010013"},{"key":"6752_CR18","doi-asserted-by":"crossref","unstructured":"Hallac D, Vare S, Boyd S, Leskovec J (2018) Toeplitz inverse covariance-based clustering of multivariate time series data. Proceed Twenty-Seventh Int Joint Conf Artif Intell, 5254\u20135258","DOI":"10.24963\/ijcai.2018\/732"},{"key":"6752_CR19","unstructured":"Jain S, Hallac D, Sosic R, Leskovec J (2018) Casc: Context-aware segmentation and clustering for motif discovery in noisy time series data. arXiv:1809.01819 124:1\u20138"},{"key":"6752_CR20","doi-asserted-by":"crossref","unstructured":"Li X, Lin J, Zhao L (2021) Time series clustering in linear time complexity. Data Mining Knowl Disc 35(6):2369\u20132388. Springer","DOI":"10.1007\/s10618-021-00798-w"},{"key":"6752_CR21","doi-asserted-by":"crossref","unstructured":"Der A, Yeh C-CM, Wu R, Wang J, Zheng Y, Zhuang Z, Wang L, Zhang W, Keogh E (2022) Matrix profile xxvii: A novel distance measure for comparing long time series. 2022 IEEE Int Conf Knowl Graph, 40\u201347. IEEE","DOI":"10.1109\/ICKG55886.2022.00013"},{"issue":"11","key":"6752_CR22","doi-asserted-by":"publisher","first-page":"11950","DOI":"10.1109\/TKDE.2022.3232331","volume":"35","author":"W Ding","year":"2023","unstructured":"Ding W, Li W, Zhang Z, Wan C, Duan J, Lu S (2023) Time-varying gaussian markov random fields learning for multivariate time series clustering. IEEE Trans Knowl Data Eng 35(11):11950\u201311966","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"6752_CR23","doi-asserted-by":"publisher","first-page":"4981","DOI":"10.1109\/TPAMI.2022.3198411","volume":"45","author":"N Zhang","year":"2023","unstructured":"Zhang N, Sun S (2023) Multiview unsupervised shapelet learning for multivariate time series clustering. IEEE Trans Pattern Anal Mach Intell 45(4):4981\u20134996","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6752_CR24","doi-asserted-by":"crossref","unstructured":"Boniol P, Tiano D, Bonifati A, Palpanas T (2025) [CDATA[ k ]]$$k$$-graph: A graph embedding for interpretable time series clustering. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2025.3543946"},{"key":"6752_CR25","doi-asserted-by":"crossref","unstructured":"Dau HA, Bagnall A, Kamgar K, Yeh C-CM, Zhu Y, Gharghabi S, Ratanamahatana CA, Keogh E (2019) The UCR time series archive. https:\/\/www.cs.ucr.edu\/~eamonn\/time_series_data_2018\/","DOI":"10.1109\/JAS.2019.1911747"},{"key":"6752_CR26","unstructured":"Cuturi M (2011) Fast global alignment kernels. Proceed 28th Int Conf Mach Learn, 929\u2013936"},{"key":"6752_CR27","unstructured":"Dimitriadou E, Hornik K (2023) Cclust:Convex clustering methods and clustering indexes. https:\/\/cran.r-project.org\/web\/packages\/cclust\/"},{"key":"6752_CR28","unstructured":"Kaufman L, Rousseeuw PJ (2009) Finding Groups in Data: an Introduction to Cluster Analysis,. John Wiley & Sons"},{"key":"6752_CR29","doi-asserted-by":"crossref","unstructured":"Paparrizos J, Gravano L (2015) k-shape: Efficient and accurate clustering of time series. Proceed 2015 ACM SIGMOD Int Conf Manage Data, 1855\u20131870","DOI":"10.1145\/2723372.2737793"},{"key":"6752_CR30","doi-asserted-by":"crossref","unstructured":"Rakthanmanon T, Keogh EJ, Lonardi S, Evans S (2011) Time series epenthesis: Clustering time series streams requires ignoring some data. 2011 IEEE 11TH Int Conf Data Mining, 547\u2013556","DOI":"10.1109\/ICDM.2011.146"},{"key":"6752_CR31","doi-asserted-by":"crossref","unstructured":"Nunthanid P, Niennattrakul V, Ratanamahatana CA (2011) Discovery of variable length time series motif. The 8th Elect Eng\/electron, Comput, Telecommun Inf Technol Ass Thailand-Conf 2011:472\u2013475","DOI":"10.1109\/ECTICON.2011.5947877"},{"key":"6752_CR32","doi-asserted-by":"crossref","unstructured":"Blalock DW, Guttag JV (2016) Extract: Strong examples from weakly-labeled sensor data. 2016 IEEE 16TH Int Conf Data Mining (ICDM), 799\u2013804","DOI":"10.1109\/ICDM.2016.0093"},{"key":"6752_CR33","doi-asserted-by":"crossref","unstructured":"Zhu Y, Yeh C-CM, Zimmerman Z, Kamgar K, Keogh E (2018) Matrix profile xi: Scrimp++: Time series motif discovery at interactive speeds. 2018 IEEE Int Conf Data Mining (ICDM), 837\u2013846","DOI":"10.1109\/ICDM.2018.00099"},{"key":"6752_CR34","doi-asserted-by":"crossref","unstructured":"Zimmerman Z, Kamgar K, Senobari NS, Crites B, Funning G, Brisk P, Keogh E (2019) Matrix profile xiv: Scaling time series motif discovery with gpus to break a quintillion pairwise comparisons a day and beyond. Proceed 2019 Tenth Acm Symp Cloud Comput, 74\u201386","DOI":"10.1145\/3357223.3362721"},{"key":"6752_CR35","doi-asserted-by":"crossref","unstructured":"Alaee S, Kamgar K, Keogh E (2020) Matrix profile xxii: Exact discovery of time series motifs under dtw. 20TH IEEE Int Conf Data Mining, 900\u2013905","DOI":"10.1109\/ICDM50108.2020.00099"},{"key":"6752_CR36","unstructured":"Reiss A (2012) PAMAP2. https:\/\/archive.ics.uci.edu\/dataset\/231\/pamap2+physical+activity+monitoring"},{"key":"6752_CR37","unstructured":"Winding data. http:\/\/alumni.cs.ucr.edu\/~rakthant\/TSEpenthesis\/Files\/Winding_data.txt"},{"key":"6752_CR38","unstructured":"Nugent C. Stock data. https:\/\/www.kaggle.com\/datasets\/camnugent\/sandp500"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06752-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06752-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06752-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T15:57:30Z","timestamp":1758297450000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06752-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,5]]},"references-count":38,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["6752"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06752-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,7,5]]},"assertion":[{"value":"21 June 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"844"}}