A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets. Zhu, Q., de Almeida Prado Alves Batista, G. E., Rakthanmanon, T., & Keogh, E. J. In Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012. Omnipress. ![A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper bibtex @InProceedings{Zhu2012,
Title = {A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets},
Author = {Qiang Zhu and Gustavo Enrique de Almeida Prado Alves Batista and Thanawin Rakthanmanon and Eamonn John Keogh},
Booktitle = {Proceedings of the Twelfth SIAM International Conference on Data Mining},
Year = {2012},
Publisher = {Omnipress},
ISBN = {9781611972320},
Url = {http://arnetminer.org/publication/a-novel-approximation-to-dynamic-time-warping-allows-anytime-clustering-of-massive-time-series-datasets-3496758.html;jsessionid=FCFDFFFABB65AB5F38AB92786FCD536E.tt}
}
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