Discovery of Periodic Patterns in Sequence Data: A Variance-Based Approach
We address the discovery of periodic patterns in sequence data. Building on prior work in this area, we present definitions and new methods for characterizing and identifying four types of periodic patterns. A unifying concept across the different types of periodic patterns we consider is the use of...
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| Veröffentlicht in: | INFORMS journal on computing 2012-06, Vol.24 (3), p.372-386 |
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| container_end_page | 386 |
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| container_issue | 3 |
| container_start_page | 372 |
| container_title | INFORMS journal on computing |
| container_volume | 24 |
| creator | Yang, Yinghui "Catherine" Padmanabhan, Balaji Liu, Hongyan Wang, Xiaoyu |
| description | We address the discovery of periodic patterns in sequence data. Building on prior work in this area, we present definitions and new methods for characterizing and identifying four types of periodic patterns. A unifying concept across the different types of periodic patterns we consider is the use of statistical variance to define periodicity. This lends itself to efficient variance-reduction algorithms for identifying periodic patterns. We motivate and test our approach using both extensive simulated sequences and real sequence data from online clickstream data. |
| doi_str_mv | 10.1287/ijoc.1110.0457 |
| format | Article |
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| ispartof | INFORMS journal on computing, 2012-06, Vol.24 (3), p.372-386 |
| issn | 1091-9856 1526-5528 1091-9856 |
| language | eng |
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| source | INFORMS PubsOnLine; Business Source Complete |
| subjects | Algorithms analysis of algorithms Data mining Forecasts and trends Fraud Fraud prevention Heuristic Information services Information services industry Methods sequential patterns Studies variance reduction |
| title | Discovery of Periodic Patterns in Sequence Data: A Variance-Based Approach |
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