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Discover Relaxed Periodicity in Temporal Databases
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Changjie Tang,
Zhonghua Yu, and
Tianqing Zhang
View Paper (PDF)
Return to Session 4A: Data Analysis and Mining
The Relaxed-Periodicity Pattern describes loose-cyclic behavior
of objects while allowing uneven stretch or shrink on time axis, limited noises,
and inflation /deflation of attribute values. To discover Relaxed-Periodicity
from Temporal Databases, we propose the concepts of Attribute Trend, Trend
Inertia, Peak-Valley Pattern, Inertia Algorithm with Anti-noise ability, as
well as the Peak-Valley Algorithm, and show that the implementation prototype
is efficient.
Copyright(C) 2000 ACM
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