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- W69342273 abstract "Previous chapter Next chapter Full AccessProceedings Proceedings of the 2002 SIAM International Conference on Data Mining (SDM)Iterative Deepening Dynamic Time Warping for Time SeriesSelina Chu, Eamonn Keogh, David Hart, and Michael PazzaniSelina ChuDepartment of Information and Computer Science, University of California, Irvine, CaliforniaDepartment of Computer Science and Engineering, University of California, Riverside, CaliforniaDepartment of Information and Computer Science, University of California, Irvine, CaliforniaDepartment of Information and Computer Science, University of California, Irvine, CaliforniaSearch for more papers by this author, Eamonn KeoghDepartment of Information and Computer Science, University of California, Irvine, CaliforniaDepartment of Computer Science and Engineering, University of California, Riverside, CaliforniaDepartment of Information and Computer Science, University of California, Irvine, CaliforniaDepartment of Information and Computer Science, University of California, Irvine, CaliforniaSearch for more papers by this author, David HartDepartment of Information and Computer Science, University of California, Irvine, CaliforniaDepartment of Computer Science and Engineering, University of California, Riverside, CaliforniaDepartment of Information and Computer Science, University of California, Irvine, CaliforniaDepartment of Information and Computer Science, University of California, Irvine, CaliforniaSearch for more papers by this author, and Michael PazzaniDepartment of Information and Computer Science, University of California, Irvine, CaliforniaDepartment of Computer Science and Engineering, University of California, Riverside, CaliforniaDepartment of Information and Computer Science, University of California, Irvine, CaliforniaDepartment of Information and Computer Science, University of California, Irvine, CaliforniaSearch for more papers by this authorpp.195 - 212Chapter DOI:https://doi.org/10.1137/1.9781611972726.12PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract 1 Introduction Time series are a ubiquitous form of data occurring in virtually every scientific discipline and business application. There has been much recent work on adapting data mining algorithms to time series databases. For example, Das et al. attempt to show how association rules can be learned from time series [7]. Debregeas and Hebrail [8] demonstrate a technique for scaling up time series clustering algorithms to massive datasets. Keogh and Pazzani introduced a new, scalable time series classification algorithm [16]. Almost all algorithms that operate on time series data need to compute the similarity between them. Euclidean distance, or some extension or modification thereof, is typically used. However as we will demonstrate in Section 2.1, Euclidean distance can be an extremely brittle distance measure. Previous chapter Next chapter RelatedDetails Published:2002ISBN:978-0-89871-517-0eISBN:978-1-61197-272-6 https://doi.org/10.1137/1.9781611972726Book Series Name:ProceedingsBook Code:PR108Book Pages:xii + 600" @default.
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