2012 MiningEventPeriodicityfromIncom

• (Li et al., 2012) ⇒ Zhenhui Li, Jingjing Wang, and Jiawei Han. (2012). “Mining Event Periodicity from Incomplete Observations.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339604

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Mining+Event+Periodicity+from+Incomplete+Observations
• http://dl.acm.org/citation.cfm?id=2339530.2339604&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; incomplete observations; periodicity

Abstract

Advanced technology in GPS and sensors enables us to track physical events, such as human movements and facility usage. Periodicity analysis from the recorded data is an important data mining task which provides useful insights into the physical events and enables us to report outliers and predict future behaviors. To mine periodicity in an event, we have to face real-world challenges of inherently complicated periodic behaviors and imperfect data collection problem. Specifically, the hidden temporal periodic behaviors could be oscillating and noisy, and the observations of the event could be incomplete.

In this paper, we propose a novel probabilistic measure for periodicity and design a practical method to detect periods. Our method has thoroughly considered the uncertainties and noises in periodic behaviors and is provably robust to incomplete observations. Comprehensive experiments on both synthetic and real datasets demonstrate the effectiveness of our method.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 MiningEventPeriodicityfromIncom	Zhenhui Li Jingjing Wang Jiawei Han			Mining Event Periodicity from Incomplete Observations				10.1145/2339530.2339604		2012