NDLI: Exact multi-length scale and mean invariant motif discovery

Content Provider	Springer Nature Link
Author	Mohammad, Yasser Nishida, Toyoaki
Copyright Year	2015
Abstract	Discovering approximately recurrent motifs (ARMs) in timeseries is an active area of research in data mining. Exact motif discovery is 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. This paper provides three extensions of the MK algorithm that allow it to find the top K similar subsequences at multiple lengths using both the Euclidean distance metric and scale invariant normalized version of it. The proposed algorithms are then applied to both synthetic data and real-world data with a focus on discovery of ARMs in human motion trajectories.
Starting Page	322
Ending Page	339
Page Count	18
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	44
Issue Number	2
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2015-07-11
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data mining Motif discovery HRI Human behavior understanding Exact motif discovery Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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Exact multi-length scale and mean invariant motif discovery