Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Analyzing Relative Motion within Groups of Trackable Moving Point Objects
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Efficient detection of motion patterns in spatio-temporal data sets
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Computing longest duration flocks in trajectory data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Efficient algorithms for mining maximal valid groups
The VLDB Journal — The International Journal on Very Large Data Bases
A prsimonious model of mobile partitioned networks with clustering
COMSNETS'09 Proceedings of the First international conference on COMmunication Systems And NETworks
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Mining mobile group patterns: a trajectory-based approach
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining multi-object spatial-temporal movement patterns
SIGSPATIAL Special
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Current literature lacks a thorough study on the discovery of meeting patterns in moving object datasets. We (a) introduced MEMO, a more precise definition of meeting patterns, (b) proposed three new algorithms based on a novel data-driven approach to extract closed MEMOs from moving object datasets and (c) implemented and evaluated them along with the algorithm previously reported in [6], whose performance has never been evaluated. Experiments using real-world datasets revealed that our filter-and-refinement algorithm outperforms the others in many realistic settings.