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Semantic Trajectory Frequent Pattern Mining Model: The Definitions and Theorems

Semantic Trajectory Frequent Pattern Mining Model: The Definitions and Theorems

Jun Li, Jie Su
Copyright: © 2022 |Volume: 18 |Issue: 1 |Pages: 20
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.297031
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MLA

Li, Jun, and Jie Su. "Semantic Trajectory Frequent Pattern Mining Model: The Definitions and Theorems." IJSWIS vol.18, no.1 2022: pp.1-20. http://doi.org/10.4018/IJSWIS.297031

APA

Li, J. & Su, J. (2022). Semantic Trajectory Frequent Pattern Mining Model: The Definitions and Theorems. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-20. http://doi.org/10.4018/IJSWIS.297031

Chicago

Li, Jun, and Jie Su. "Semantic Trajectory Frequent Pattern Mining Model: The Definitions and Theorems," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-20. http://doi.org/10.4018/IJSWIS.297031

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Abstract

A method for mining frequent patterns of individual user trajectories is proposed based on location semantics. The semantic trajectory is obtained by inverse geocoding and preprocessed to obtain the Top-k candidate frequent location item sets, and then the spatio-temporal sequence intersection and the divide and conquer merge methods are used to convert the frequent iterative calculation of long itemsets into hierarchical sets' regular operations, the superset and subset of frequent sequences are found. This kind of semantic trajectory frequent pattern mining can actively identify and discover potential carpooling needs, and provide higher accuracy for location-based intelligent recommendations such as carpooling and HOV lane travel (High-Occupancy Vehicle Lane). Carpool matching and recommendation based on semantic trajectory in this paper is suitable for single carpooling and relay-ride carpooling. the results of simulation carpooling experiments prove the applicability and efficiency of the method.