{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:06:19Z","timestamp":1775912779061,"version":"3.50.1"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:p>Clustering multivariate time series is a critical task in many real-world applications involving multiple signals and sensors. Existing systems aim to maximize effectiveness, efficiency and scalability, but fail to guarantee the interpretability of the results. This hinders their application in critical real scenarios where human comprehension of algorithmic behavior is required. This paper introduces Time2Feat, an end-to-end machine learning system for multivariate time series (MTS) clustering. The system relies on inter-signal and intra-signal interpretable features extracted from the time series. Then, a dimensionality reduction technique is applied to select a subset of features that retain most of the information, thus enhancing the interpretability of the results. In addition, domain experts can semi-supervise the process, by providing a small amount of MTS with a target cluster. This process further improves both accuracy and interpretability, narrowing down the number of features used by the clustering process. We demonstrate the effectiveness, interpretability, efficiency, and robustness of Time2Feat through experiments on eighteen benchmarking time series datasets, comparing them with state-of-the-art MTS clustering methods.<\/jats:p>","DOI":"10.14778\/3565816.3565822","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T00:35:16Z","timestamp":1669250116000},"page":"193-201","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Time2Feat"],"prefix":"10.14778","volume":"16","author":[{"given":"Angela","family":"Bonifati","sequence":"first","affiliation":[{"name":"Lyon 1 University"}]},{"given":"Francesco Del","family":"Buono","sequence":"additional","affiliation":[{"name":"University of Modena and Reggio Emilia"}]},{"given":"Francesco","family":"Guerra","sequence":"additional","affiliation":[{"name":"University of Modena and Reggio Emilia"}]},{"given":"Donato","family":"Tiano","sequence":"additional","affiliation":[{"name":"Lyon 1 University"}]}],"member":"320","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"3001","article-title":"Deep Time-Series Clustering","volume":"10","author":"Alqahtani Ali","year":"2021","unstructured":"Ali Alqahtani , Mohammed Ali , Xianghua Xie , and Mark W Jones . 2021 . Deep Time-Series Clustering : A Review. Electronics 10 , 23 (2021), 3001 . Ali Alqahtani, Mohammed Ali, Xianghua Xie, and Mark W Jones. 2021. Deep Time-Series Clustering: A Review. Electronics 10, 23 (2021), 3001.","journal-title":"A Review. Electronics"},{"key":"e_1_2_1_3_1","volume-title":"Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn J. Keogh.","author":"Bagnall Anthony J.","year":"2018","unstructured":"Anthony J. Bagnall , Hoang Anh Dau , Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn J. Keogh. 2018 . The UEA multivariate time series classification archive, 2018. CoRR abs\/1811.00075 (2018). Anthony J. Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn J. Keogh. 2018. The UEA multivariate time series classification archive, 2018. CoRR abs\/1811.00075 (2018)."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-020-05896-2"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2017.2783242"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.03.067"},{"key":"e_1_2_1_7_1","volume-title":"Distributed and parallel time series feature extraction for industrial big data applications. CoRR abs\/1610.07717","author":"Christ Maximilian","year":"2016","unstructured":"Maximilian Christ , Andreas W. Kempa-Liehr , and Michael Feindt . 2016. Distributed and parallel time series feature extraction for industrial big data applications. CoRR abs\/1610.07717 ( 2016 ). Maximilian Christ, Andreas W. Kempa-Liehr, and Michael Feindt. 2016. Distributed and parallel time series feature extraction for industrial big data applications. CoRR abs\/1610.07717 (2016)."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1391"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/app12104931"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359786"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.04.003"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11634-013-0129-3"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47426-3_25"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.11.013"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/sam.11461"},{"key":"e_1_2_1_16_1","volume-title":"GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction. In KDD. ACM, 238--248.","author":"Le Thai","year":"2020","unstructured":"Thai Le , Suhang Wang , and Dongwon Lee . 2020 . GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction. In KDD. ACM, 238--248. Thai Le, Suhang Wang, and Dongwon Lee. 2020. GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction. In KDD. ACM, 238--248."},{"key":"e_1_2_1_17_1","volume-title":"Asynchronism-based principal component analysis for time series data mining. Expert systems with applications 41, 6","author":"Hailin Li.","year":"2014","unstructured":"Hailin Li. 2014. Asynchronism-based principal component analysis for time series data mining. Expert systems with applications 41, 6 ( 2014 ), 2842--2850. Hailin Li. 2014. Asynchronism-based principal component analysis for time series data mining. Expert systems with applications 41, 6 (2014), 2842--2850."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.03.060"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2915602"},{"key":"e_1_2_1_20_1","unstructured":"Zhihan Li Youjian Zhao Jiaqi Han Ya Su Rui Jiao Xidao Wen and Dan Pei. 2021. Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding. In KDD. ACM 3220--3230. Zhihan Li Youjian Zhao Jiaqi Han Ya Su Rui Jiao Xidao Wen and Dan Pei. 2021. Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding. In KDD. ACM 3220--3230."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1291233.1291297"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2017.11"},{"key":"e_1_2_1_23_1","volume-title":"DPSOM: Deep probabilistic clustering with self-organizing maps. arXiv preprint arXiv:1910.01590","author":"Manduchi Laura","year":"2019","unstructured":"Laura Manduchi , Matthias H\u00fcser , Julia Vogt , Gunnar R\u00e4tsch , and Vincent Fortuin . 2019 . DPSOM: Deep probabilistic clustering with self-organizing maps. arXiv preprint arXiv:1910.01590 (2019). Laura Manduchi, Matthias H\u00fcser, Julia Vogt, Gunnar R\u00e4tsch, and Vincent Fortuin. 2019. DPSOM: Deep probabilistic clustering with self-organizing maps. arXiv preprint arXiv:1910.01590 (2019)."},{"key":"e_1_2_1_24_1","volume-title":"Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence 267","author":"Miller Tim","year":"2019","unstructured":"Tim Miller . 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence 267 ( 2019 ), 1--38. Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence 267 (2019), 1--38."},{"key":"e_1_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Christoph Molnar. 2019. Interpretable Machine Learning. https:\/\/christophm.github.io\/interpretable-ml-book\/. Christoph Molnar. 2019. Interpretable Machine Learning. https:\/\/christophm.github.io\/interpretable-ml-book\/.","DOI":"10.21105\/joss.00786"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.5555\/1324818"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1900654116"},{"key":"e_1_2_1_28_1","volume-title":"ICML (ACM International Conference Proceeding Series)","volume":"382","author":"Nguyen Xuan Vinh","year":"2009","unstructured":"Xuan Vinh Nguyen , Julien Epps , and James Bailey . 2009 . Information theoretic measures for clusterings comparison: is a correction for chance necessary? . In ICML (ACM International Conference Proceeding Series) , Vol. 382 . ACM, 1073--1080. Xuan Vinh Nguyen, Julien Epps, and James Bailey. 2009. Information theoretic measures for clusterings comparison: is a correction for chance necessary?. In ICML (ACM International Conference Proceeding Series), Vol. 382. ACM, 1073--1080."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2020.101549"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2737793"},{"key":"e_1_2_1_31_1","unstructured":"Jayneel Parekh Pavlo Mozharovskyi and Florence d'Alch\u00e9-Buc. 2021. A Framework to Learn with Interpretation. In NeurIPS. 24273--24285. Jayneel Parekh Pavlo Mozharovskyi and Florence d'Alch\u00e9-Buc. 2021. A Framework to Learn with Interpretation. In NeurIPS. 24273--24285."},{"key":"e_1_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Claudia Plant and Christian B\u00f6hm. 2011. INCONCO: interpretable clustering of numerical and categorical objects. In KDD. ACM 1127--1135. Claudia Plant and Christian B\u00f6hm. 2011. INCONCO: interpretable clustering of numerical and categorical objects. In KDD. ACM 1127--1135.","DOI":"10.1145\/2020408.2020584"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.3115\/1117575.1117578"},{"key":"e_1_2_1_34_1","unstructured":"Andrew Rosenberg and Julia Hirschberg. 2007. V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure. In EMNLP-CoNLL. ACL 410--420. Andrew Rosenberg and Julia Hirschberg. 2007. V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure. In EMNLP-CoNLL. ACL 410--420."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2019.109384"},{"key":"e_1_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Sandhya Saisubramanian Sainyam Galhotra and Shlomo Zilberstein. 2020. Balancing the Tradeoff Between Clustering Value and Interpretability. In AIES. ACM 351--357. Sandhya Saisubramanian Sainyam Galhotra and Shlomo Zilberstein. 2020. Balancing the Tradeoff Between Clustering Value and Interpretability. In AIES. ACM 351--357.","DOI":"10.1145\/3375627.3375843"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00231"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00180-016-0667-1"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103457"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10182-013-0213-1"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05815-0"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1002\/cem.945"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-7439(89)80095-4"},{"key":"e_1_2_1_45_1","volume-title":"Properties of the Hubert-Arable Adjusted Rand Index. Psychological methods 9, 3","author":"Steinley Douglas","year":"2004","unstructured":"Douglas Steinley . 2004. Properties of the Hubert-Arable Adjusted Rand Index. Psychological methods 9, 3 ( 2004 ), 386. Douglas Steinley. 2004. Properties of the Hubert-Arable Adjusted Rand Index. Psychological methods 9, 3 (2004), 386."},{"key":"e_1_2_1_46_1","first-page":"2784","article-title":"FeatTS","volume":"2021","author":"Tiano Donato","year":"2021","unstructured":"Donato Tiano , Angela Bonifati , and Raymond Ng . 2021 . FeatTS : Feature-based Time Series Clustering. In SIGMOD 2021. 2784 -- 2788 . Donato Tiano, Angela Bonifati, and Raymond Ng. 2021. FeatTS: Feature-based Time Series Clustering. In SIGMOD 2021. 2784--2788.","journal-title":"Feature-based Time Series Clustering. In SIGMOD"},{"key":"e_1_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Donato Tiano Angela Bonifati and Raymond Ng. 2021. Feature-driven Time Series Clustering.. In EDBT. 349--354. Donato Tiano Angela Bonifati and Raymond Ng. 2021. Feature-driven Time Series Clustering.. In EDBT. 349--354.","DOI":"10.1145\/3448016.3452757"},{"key":"e_1_2_1_48_1","first-page":"2579","article-title":"Visualizing High-Dimensional Data Using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey E. Hinton . 2008 . Visualizing High-Dimensional Data Using t-SNE . Journal of Machine Learning Research 9 (2008), 2579 -- 2605 . Laurens van der Maaten and Geoffrey E. Hinton. 2008. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9 (2008), 2579--2605.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_49_1","volume-title":"A Deep Neural Network for Multivariate Time Series Clustering with Result Interpretation","author":"Xu Chenxiao","unstructured":"Chenxiao Xu , Hao Huang , and Shinjae Yoo . 2021. A Deep Neural Network for Multivariate Time Series Clustering with Result Interpretation . In IJCNN. IEEE , 1--8. Chenxiao Xu, Hao Huang, and Shinjae Yoo. 2021. A Deep Neural Network for Multivariate Time Series Clustering with Result Interpretation. In IJCNN. IEEE, 1--8."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2980079"},{"key":"e_1_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Zijian Zhang Koustav Rudra and Avishek Anand. 2021. Explain and Predict and then Predict Again. In WSDM. ACM 418--426. Zijian Zhang Koustav Rudra and Avishek Anand. 2021. Explain and Predict and then Predict Again. In WSDM. ACM 418--426.","DOI":"10.1145\/3437963.3441758"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544903.3544905"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3565816.3565822","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T12:40:37Z","timestamp":1728477637000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3565816.3565822"}},"subtitle":["learning interpretable representations for multivariate time series clustering"],"short-title":[],"issued":{"date-parts":[[2022,10]]},"references-count":51,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["10.14778\/3565816.3565822"],"URL":"https:\/\/doi.org\/10.14778\/3565816.3565822","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,10]]},"assertion":[{"value":"2022-11-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}