Semantic Trajectory Frequent Pattern Mining Model

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: International Journal on Semantic Web and Information Systems

سال: 2022

ISSN: ['1552-6291', '1552-6283']

DOI: https://doi.org/10.4018/ijswis.297031