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Among these, time series clustering (TSCL) stands out as one of the most popular machine learning tasks. TSCL serves as a powerful exploratory analysis tool and is also employed as a preprocessing step or subroutine for various tasks, including anomaly detection, segmentation, and classification. The most popular TSCL algorithms are either fast (in terms of runtime) but perform poorly on benchmark problems, or perform well on benchmarks but scale poorly. We present a new TSCL algorithm, the\n                    <jats:italic>k<\/jats:italic>\n                    -means (K) Accelerated (A) Stochastic subgradient (S) Barycentre (B) Average (A) (KASBA) clustering algorithm. KASBA is a\n                    <jats:italic>k<\/jats:italic>\n                    -means clustering algorithm that uses the Move-Split-Merge (MSM) elastic distance at all stages of clustering, applies a randomised stochastic subgradient descent to find barycentre centroids, links each stage of clustering to accelerate convergence and exploits the metric property of MSM distance to avoid a large proportion of distance calculations. It is a versatile and scalable clusterer designed for real-world TSCL applications. It allows practitioners to balance runtime and clustering performance when similarity is best measured by an elastic distance. We demonstrate through extensive experimentation that KASBA matches the current shape based state of the art clusterers and offers orders of magnitude improvement in runtime over the most performant elastic distance based\n                    <jats:italic>k<\/jats:italic>\n                    -means alternatives.\n                  <\/jats:p>","DOI":"10.1007\/s10618-026-01189-9","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T14:12:55Z","timestamp":1772028775000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Rock the KASBA: blazingly fast and accurate time series clustering"],"prefix":"10.1007","volume":"40","author":[{"given":"Christopher","family":"Holder","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anthony","family":"Bagnall","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"issue":"2","key":"1189_CR1","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1007\/s10618-018-0596-4","volume":"33","author":"A Abanda","year":"2019","unstructured":"Abanda A, Mori U, Lozano J (2019) A review on distance based time series classification. 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The authors have no Conflict of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"In line with the FAIR principles (Findable, Accessible, Interoperable, and Reusable), all datasets used in this study are publicly available from the University of California, Riverside (UCR) Time Series Archive\u00a0(Dau et al.\n                      \n                      )(\n                      \n                      ). All datasets are also available from zenodo(\n                      \n                      ) and can be downloaded as a single zip file\u00a0(\n                      \n                      ). The source code and experimental workflows are implemented using the open-source\n                      aeon\n                      toolkit\u00a0(Middlehurst et al.\n                      \n                      )(\n                      \n                      ) and the tsml-eval evaluation suite(\n                      \n                      ). KASBA is available as scikit-learn compatible estimator in\n                      aeon\n                      release 1.1 as are most of the other clustering algorithms we use. All experiments, results, and reproduction scripts for this paper are available in the repository associated with this paper(\n                      \n                      ). All code is released under the BSD 3-Clause License, and full documentation is provided to support reuse, extension, and validation by other researchers.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"21"}}