NDLI: Mining periodic patterns from floating and ambiguous time segments

Content Provider	IEEE Xplore Digital Library
Author	Chih Lai Nguyen, N.T. Nelson, D.E.
Copyright Year	2005
Description	Author affiliation: Graduate Program in Software Eng., St. Thomas Univ., St. Paul, MN, USA (Chih Lai; Nguyen, N.T.)
Abstract	A partial periodic pattern is referred to as a set of events that exhibits cyclic behavior over some periods in a time series. Previous studies focused on mining such patterns from constant-length segments that are always stationary between fixed offsets within periods. Unfortunately, many objects under study, like mice, seldom align their fluctuating behaviors with stationary segments that are created based on artificial concepts such as hours. As the result, many patterns that occur across artificial boundaries may not be detected. In this paper, we present a more flexible model that can dynamically create floating segments from a time series such that each segment captures one type of sporadic activity, such as running, over irregular length of time. Temporal sections that are frequently overlapped by floating segments are detected as 1-section patterns. Combinations of some 1-section patterns, referred to as K-section patterns, can then be discovered by the Apriori algorithm.
Starting Page	3622
Ending Page	3627
File Size	262261
Page Count	6
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571710
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mice Wheels Software engineering Biology Uncertainty Drugs Pattern analysis Pattern matching Frequency uncertainty Mining floating segments
Content Type	Text
Resource Type	Article

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