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]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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