Abstract
Discovering approximately recurrent motifs (ARMs) in timeseries is an active area of research in data mining. Exact motif discovery was later defined as the problem of efficiently finding the most similar pairs of timeseries subsequences and can be used as a basis for discovering ARMs. The most efficient algorithm for solving this problem is the MK algorithm which was designed to find a single pair of timeseries subsequences with maximum similarity at a known length. Available exact solutions to the problem of finding top K similar subsequence pairs at multiple lengths (which can be the basis of ARM discovery) are not scale invariant. This paper proposes a new algorithm for solving this problem efficiently using scale invariant distance functions and applies it to both real and synthetic dataset.
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Mohammad, Y., Nishida, T. (2014). Scale Invariant Multi-length Motif Discovery. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8482. Springer, Cham. https://doi.org/10.1007/978-3-319-07467-2_44
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DOI: https://doi.org/10.1007/978-3-319-07467-2_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07466-5
Online ISBN: 978-3-319-07467-2
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