reinterpolate).Please see the documentation for dtwclust, which serves as the main entry point.
Other packages that are particularly leveraged here are the flexclust package for partitional
clustering, the proxy package for distance matrix calculations, and the dtw package for the
core DTW calculations.
Five distances are registered via pr_DB: "LB_Keogh", "LB_Improved", "SBD", "DTW2"
and "DTW_LB". See lb_keogh, lb_improved and SBD for more
details on the first 3. DTW2 is done with dtw using L2 norm, but it
differs from the result you would obtain if you specify L2 as dist.method: with DTW2,
pointwise distances (the local cost matrix) are calculated with L1 norm, each element of the
matrix is squared and the result is fed into dtw, which finds the optimum warping path.
The square root of the resulting distance is then computed. See dtw_lb for the last one.
Please note that the dist function in the proxy package accepts one or two
arguments for data objects. Users should usually use the two-input list version, even if there is
just one dataset (i.e. proxy::dist(x=data, y=data, ...)), because otherwise it sometimes fails to
detect a whole time series as a single object and, instead, calculates distances between each observation
of each time series.
Giorgino T (2009). ``Computing and Visualizing Dynamic Time Warping Alignments in R: The 'dtw' Package.'' Journal
of Statistical Software, 31(7), pp. 1-24.
Ratanamahatana A and Keogh E (2004). ``Everything you know about dynamic time warping is wrong.'' In 3rd Workshop on Mining Temporal and Sequential Data, in conjunction with 10th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD-2004), Seattle, WA.
Keogh E and Ratanamahatana CA (2005). ``Exact indexing of dynamic time warping.'' Knowledge and information systems, 7(3), pp. 358-386.
Lemire D (2009). ``Faster retrieval with a two-pass dynamic-time-warping lower bound .'' Pattern Recognition, 42(9), pp.
2169 - 2180. ISSN 0031-3203,
Liao TW (2005). ``Clustering of time series data - a survey.'' Pattern recognition, 38(11), pp. 1857-1874.
Paparrizos J and Gravano L (2015). ``k-Shape: Efficient and Accurate Clustering of Time Series.'' In Proceedings of the 2015
ACM SIGMOD International Conference on Management of Data, series SIGMOD '15, pp. 1855-1870. ISBN 978-1-4503-2758-9,
Petitjean F, Ketterlin A and Gancarski P (2011). ``A global averaging method for dynamic time warping, with applications to
clustering.'' Pattern Recognition, 44(3), pp. 678 - 693. ISSN 0031-3203,
Sakoe H and Chiba S (1978). ``Dynamic programming algorithm optimization for spoken word recognition.'' Acoustics, Speech and
Signal Processing, IEEE Transactions on, 26(1), pp. 43-49. ISSN 0096-3518,
Wang X, Mueen A, Ding H, Trajcevski G, Scheuermann P and Keogh E (2013). ``Experimental comparison of representation methods
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dtwclust, kcca, dist,
dtw