The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Efficient mining of partial periodic patterns in time series database
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.
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Cites
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Bayardo, R. J. (1998) · ACM SIGMOD Record · doi:10.1145/276305.276313 · Cited by 398
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Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Self-citationFrequent pattern mining: current status and future directions
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Self-citation(2003) · Cited by 165
Constrained frequent pattern mining: a pattern-growth view
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Metadata
- Authors
- Han, J., Dong, G., & Yin, Y.
- Venue
- Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337)
- Publisher
- IEEE
- Published
- 1999
- Pages
- 106-115
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W2126310301
- Cited by
- 587
- References
- 10
- FWCI
- 23.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Han, J., Dong, G., & Yin, Y. (1999). Efficient mining of partial periodic patterns in time series database. Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337). https://doi.org/10.1109/icde.1999.754913
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