NDLI: A robust periodicity mining method from incomplete and noisy observations based on relative entropy

Content Provider	Springer Nature Link
Author	Guan, Tao Wang, Ke ren Zhang, Shao peng
Copyright Year	2015
Abstract	A long-standing problem in data mining is the accurate estimation of the period of a periodic process. To mine periodicity in an event, we have to face real-world challenges of inherently uncertain periodic behaviors and imperfect data collection. This paper presents a method for periodicity detection from incomplete and noisy observations. Firstly, we review previous works and point out its defects for specific periodic patterns. Secondly, a novel relative entropy based measure is proposed, and its validity is proved in a probabilistic framework. Experiments on simulated event sequences of various configurations and real-world human movement datasets show the effectiveness of our method. And performance results demonstrate that our scheme outperforms previous works generally, especially when the time interval ratio is quite small or large.
Starting Page	283
Ending Page	293
Page Count	11
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	8
Issue Number	1
e-ISSN	1868808X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-01-07
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Periodicity detection Relative entropy Incomplete observations Periodic patterns Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Complex Systems Systems Biology Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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