Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Framework for Generating Network-Based Moving Objects
Geoinformatica
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
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PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
WUM - A Tool for WWW Ulitization Analysis
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Temporal moving pattern mining for location-based service
Journal of Systems and Software
A Novel Trajectory Pattern Learning Method Based on Sequential Pattern Mining
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Efficient mining and prediction of user behavior patterns in mobile web systems
Information and Software Technology
A Proposal of Spatio-Temporal Pattern Queries
CISIS '10 Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems
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This paper presents the FCP-Tree index structure and the new algorithm for continuous pattern mining, called FCPGrowth, for Trajectory Data Warehouses The FCP-Tree is an aggregate tree which allows storing similar sequences in the same nodes A characteristic feature of the FCPGrowth algorithm is that it does not require constructing intermediate trees at recursion levels and therefore, it has small memory requirements In addition, when the initial FCP-Tree is built, input sequences are split on infrequent elements, thereby increasing the compactness of this structure The FCPGrowth algorithm is much more efficient than our previous algorithm, which is confirmed experimentally in this paper.