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Efficient Mining of Partial Periodic Patterns in Time Series Database

Jiawei Han,
School of Computing Science,
Simon Fraser University,
han@cs.sfu.ca

Guozhu Dong,
Department of Computer Science and Engineering,
Wright State University,
gdong@cs.wright.edu

Yiwen Yin ,
School of Computing Science ,
Simon Fraser University,
yiweny@cs.sfu.ca

IEEE International Conference on Data Engineering (ICDE), Sydney, March, 1999.

#### Abstract

Partial periodicity search, i.e., search for partial periodic patterns in
time-series databases, is an interesting data mining problem. Previous
studies on periodicity search mainly consider finding full periodic patterns,
where every point in time contributes (precisely or approximately) to the
periodicity. However, partial periodicity is very common in practice since
it is more likely that only some of the time episodes may exhibit periodic
patterns.
We present several algorithms for efficient mining of partial periodic
patterns, by exploring some interesting properties related to partial
periodicity, such as the Apriori property and the max-subpattern hit
set property, and by shared mining of multiple periods. The max-subpattern
hit set property is a vital new property which allows us to derive the
counts of all frequent patterns from a relatively small subset of patterns
existing in the time series. We show that mining partial periodicity needs
only two scans over the time series database, even for mining multiple periods.
The performance study shows our proposed methods are very efficient in mining
long periodic patterns.

Keywords:Periodicity search, partial periodicity, time-series analysis, data
mining algorithms.