{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:16:36Z","timestamp":1773803796214,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"31","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Current unsupervised time series clustering methods often struggle to fully exploit the inherent characteristics of time series data and commonly adopt a two-stage training strategy that separates feature learning from the clustering process. To address these limitations, this paper proposes a novel deep clustering framework, Time-Frequency augmented Multi-level Contrastive Clustering (TFMCC). TFMCC employs a multi-scale time-frequency augmentation strategy, where each training iteration stochastically selects time and frequency scales to generate diverse augmented views, enhancing the model\u2019s ability to learn robust and generalizable representations. In addition, a multi-level contrastive learning mechanism is introduced to jointly capture temporal dependencies, inter-sample similarities, and cluster structures. By jointly optimizing these components, TFMCC enables the learning of temporally-aware and clustering-friendly representations. Experimental results on 40 benchmark datasets demonstrate that TFMCC outperforms six existing methods in clustering accuracy.<\/jats:p>","DOI":"10.1609\/aaai.v40i31.39817","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:06:12Z","timestamp":1773799572000},"page":"26142-26150","source":"Crossref","is-referenced-by-count":0,"title":["Time-Frequency Augmented Multi-level Contrastive Clustering for Time Series"],"prefix":"10.1609","volume":"40","author":[{"given":"Congyu","family":"Wang","sequence":"first","affiliation":[]},{"given":"Mingjing","family":"Du","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39817\/43778","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39817\/43778","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:06:12Z","timestamp":1773799572000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/39817"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"31","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i31.39817","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}