Abstract
This paper presents an approach for mining partial periodic association rules in temporal databases. This approach allows the discovery of periodic episodes such that the events in an episode are not limited to a fixed order. Moreover, this approach treats the antecedent and consequent of a rule separately and allows time lag between them. Thus, rules discovered are useful in many applications for prediction.
Original language | English (US) |
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Pages | 427-428 |
Number of pages | 2 |
DOIs | |
State | Published - 2006 |
Externally published | Yes |
Event | 7th Annual International Conference on Digital Government Research, Dg.o 2006 - San Diego, CA, United States Duration: May 21 2006 → May 24 2006 |
Conference
Conference | 7th Annual International Conference on Digital Government Research, Dg.o 2006 |
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Country/Territory | United States |
City | San Diego, CA |
Period | 5/21/06 → 5/24/06 |
ASJC Scopus subject areas
- Software
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications