Maintaining knowledge about temporal intervals
Communications of the ACM
Theoretical frameworks for data mining
ACM SIGKDD Explorations Newsletter
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Modeling and Predicting Future Trajectories of Moving Objects in a Constrained Network
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
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Considering technical difficulties and bottlenecks in moving objects data mining, such as massive movement data, high dimensional data, topological complexity, and knowledge semantic representation etc., this paper focuses on the study of theory and methods of moving objects data mining. First, it presents two key scientific issues of the research topic, i.e. integration and modeling of heterogeneous data, and information aggregation and interpretation. Second, a theoretical framework of moving object data mining is proposed based on different perspectives of "space-time data→space-time concept→space-time pattern". Two aspects of the framework are then discussed in details, including (1) moving objects data modeling and semantic expression; (2) mining methods and algorithms of association rules based on concept lattice. Finally, future works are discussed.