Abstract
We present a framework for mining frequent trajectories, which are translated and/or rotated with respect to one another. We then discuss a multiresolution methodology, based on the wavelet transformation, for speeding up the discovery of frequent trajectories. We present experimental results using noisy protein unfolding trajectories and synthetic datasets. Our results demonstrate the effectiveness of the proposed approaches for finding frequent trajectories. A multiresolution mining strategy provides significant mining speed improvements.
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Andreopoulos, A., Andreopoulos, B., An, A., Wang, X. (2007). Translation and Rotation Invariant Mining of Frequent Trajectories: Application to Protein Unfolding Pathways. In: Washio, T., et al. Emerging Technologies in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77018-3_19
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DOI: https://doi.org/10.1007/978-3-540-77018-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77016-9
Online ISBN: 978-3-540-77018-3
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