{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T04:09:33Z","timestamp":1750392573234,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819682942","type":"print"},{"value":"9789819682959","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-8295-9_30","type":"book-chapter","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T17:47:21Z","timestamp":1750355241000},"page":"407-419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Extraction of\u00a0Variable-Length Subsequence Patterns in\u00a0Noisy Time Series"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6612-1924","authenticated-orcid":false,"given":"Ke","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5095-2339","authenticated-orcid":false,"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":"Hongfei","family":"Wang","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":[[2025,6,20]]},"reference":[{"key":"30_CR1","unstructured":"Physiobank atm. https:\/\/archive.physionet.org\/cgi-bin\/atm\/ATM"},{"key":"30_CR2","unstructured":"Temperature data. http:\/\/alumni.cs.ucr.edu\/~rakthant\/TSEpenthesis\/"},{"key":"30_CR3","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.is.2015.04.007","volume":"53","author":"S Aghabozorgi","year":"2015","unstructured":"Aghabozorgi, S., Shirkhorshidi, A.S., Wah, T.Y.: Time-series clustering-a decade review. Inf. Syst. 53, 16\u201338 (2015)","journal-title":"Inf. Syst."},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Alaee, S., Kamgar, K., Keogh, E.: Matrix profile XXII: exact discovery of time series motifs under DTW. In: ICDM, pp. 900\u2013905 (2020)","DOI":"10.1109\/ICDM50108.2020.00099"},{"key":"30_CR5","unstructured":"Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer (2013)"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Blalock, D.W., Guttag, J.V.: Extract: strong examples from weakly-labeled sensor data. In: ICDM, pp. 799\u2013804 (2016)","DOI":"10.1109\/ICDM.2016.0093"},{"issue":"1","key":"30_CR7","doi-asserted-by":"publisher","first-page":"219","DOI":"10.3390\/forecast4010013","volume":"4","author":"E Cartwright","year":"2022","unstructured":"Cartwright, E., Crane, M., Ruskin, H.J.: Side-length-independent motif (SLIM): Motif discovery and volatility analysis in time series-SAX, MDL and the matrix profile. Forcasting 4(1), 219\u2013237 (2022)","journal-title":"Forcasting"},{"issue":"11","key":"30_CR8","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1016\/j.neucom.2010.11.026","volume":"74","author":"A Cherif","year":"2011","unstructured":"Cherif, A., Cardot, H., Bon\u00e9, R.: SOM time series clustering and prediction with recurrent neural networks. Neurocomputing 74(11), 1936\u20131944 (2011)","journal-title":"Neurocomputing"},{"key":"30_CR9","unstructured":"Cuturi, M.: Fast global alignment kernels. In: ICML, pp. 929\u2013936 (2011)"},{"key":"30_CR10","unstructured":"Das, G., Lin, K.I., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series. In: KDD, vol.\u00a098, pp. 16\u201322 (1998)"},{"key":"30_CR11","unstructured":"Dimitriadou, E., Hornik, K.: Cclust: convex clustering methods and clustering indexes (2023). https:\/\/cran.r-project.org\/web\/packages\/cclust\/"},{"issue":"2","key":"30_CR12","first-page":"983","volume":"34","author":"J Duan","year":"2022","unstructured":"Duan, J., Guo, L.: Variable-length subsequence clustering in time series. TKDE 34(2), 983\u2013995 (2022)","journal-title":"TKDE"},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Ezugwu, A.E., et al.: 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) (2022)","DOI":"10.1016\/j.engappai.2022.104743"},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Hallac, D., Vare, S., Boyd, S., Leskovec, J.: Toeplitz inverse covariance-based clustering of multivariate time series data. In: IJCAI, pp. 5254\u20135258 (2018)","DOI":"10.24963\/ijcai.2018\/732"},{"key":"30_CR15","unstructured":"Jain, S., Hallac, D., Sosic, R., Leskovec, J.: CASC: context-aware segmentation and clustering for motif discovery in noisy time series data. arXiv preprint arXiv:1809.01819124, 1\u20138 (2018)"},{"key":"30_CR16","unstructured":"Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley (2009)"},{"issue":"2","key":"30_CR17","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s10115-004-0172-7","volume":"8","author":"E Keogh","year":"2005","unstructured":"Keogh, E., Lin, J.: Clustering of time-series subsequences is meaningless: implications for previous and future research. Knowl. Inf. Syst. 8(2), 154\u2013177 (2005)","journal-title":"Knowl. Inf. Syst."},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Linardi, M., et al.: Matrix profile X: VALMOD-scalable discovery of variable-length motifs in data series. In: COMAD, pp. 1053\u20131066 (2018)","DOI":"10.1145\/3183713.3183744"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Nunthanid, P., Niennattrakul, V., Ratanamahatana, C.A.: Discovery of variable length time series motif. In: ECIT, ThaiLand, pp. 472\u2013475 (2011)","DOI":"10.1109\/ECTICON.2011.5947877"},{"key":"30_CR20","doi-asserted-by":"crossref","unstructured":"Paparrizos, J., Gravano, L.: k-shape: efficient and accurate clustering of time series. In: SIGMOD, pp. 1855\u20131870 (2015)","DOI":"10.1145\/2723372.2737793"},{"key":"30_CR21","doi-asserted-by":"crossref","unstructured":"Rakthanmanon, T., Keogh, E.J., Lonardi, S., Evans, S.: Time series epenthesis: clustering time series streams requires ignoring some data. In: ICDM, pp. 547\u2013556 (2011)","DOI":"10.1109\/ICDM.2011.146"},{"key":"30_CR22","unstructured":"Williams, L., O\u2019Kane, C.O., Franks, A.L.: Numenta anomaly benchmark: a benchmark for anomaly detection algorithms (2016). https:\/\/github.com\/numenta\/NAB"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, K., et al.: Self-supervised learning for time series analysis: taxonomy, progress, and prospects. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3387317"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Yeh, C.C.M., Zimmerman, Z., Kamgar, K., Keogh, E.: Matrix profile XI: SCRIMP++ : time series motif discovery at interactive speeds. In: ICDM, pp. 837\u2013846 (2018)","DOI":"10.1109\/ICDM.2018.00099"}],"container-title":["Lecture Notes in Computer Science","Data Science: Foundations and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8295-9_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T17:47:26Z","timestamp":1750355246000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8295-9_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819682942","9789819682959"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8295-9_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"20 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}