A novel clustering-based method for time series motif discovery under time warping measure

A novel clustering-based method for time series motif discovery under time warping measure The problem of time series motif discovery has attracted a lot of attention and is useful in many real-world applications. However, most of the proposed methods so far use Euclidean distance to deal with this problem. There has been one proposed method, called MDTW_WedgeTree, for time series motif discovery under DTW distance. But this method aims to deal with the case in which motif is the time series in a time series database which has the highest count of its similar time series within a range r. To adapt the above-mentioned method to the case in which motifs are frequently occurring subsequences of a longer time series, we modify MDTW_WedgeTree to a new algorithm for discovering “subsequence” motifs in time series under DTW. The proposed method consists of a segmentation method to divide the time series into motif candidates and a BIRCH-based clustering which can efficiently cluster motif candidate subsequences under DTW distance. Experimental results showed that our proposed method for discovering “subsequence” motifs performs very efficiently on large time series datasets while brings out high accuracy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Data Science and Analytics Springer Journals

A novel clustering-based method for time series motif discovery under time warping measure

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Publisher
Springer International Publishing
Copyright
Copyright © 2017 by Springer International Publishing AG
Subject
Computer Science; Data Mining and Knowledge Discovery; Database Management; Artificial Intelligence (incl. Robotics); Computational Biology/Bioinformatics; Business Information Systems
ISSN
2364-415X
eISSN
2364-4168
D.O.I.
10.1007/s41060-017-0060-3
Publisher site
See Article on Publisher Site

Abstract

The problem of time series motif discovery has attracted a lot of attention and is useful in many real-world applications. However, most of the proposed methods so far use Euclidean distance to deal with this problem. There has been one proposed method, called MDTW_WedgeTree, for time series motif discovery under DTW distance. But this method aims to deal with the case in which motif is the time series in a time series database which has the highest count of its similar time series within a range r. To adapt the above-mentioned method to the case in which motifs are frequently occurring subsequences of a longer time series, we modify MDTW_WedgeTree to a new algorithm for discovering “subsequence” motifs in time series under DTW. The proposed method consists of a segmentation method to divide the time series into motif candidates and a BIRCH-based clustering which can efficiently cluster motif candidate subsequences under DTW distance. Experimental results showed that our proposed method for discovering “subsequence” motifs performs very efficiently on large time series datasets while brings out high accuracy.

Journal

International Journal of Data Science and AnalyticsSpringer Journals

Published: Jun 30, 2017

References

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