The design and analysis of spatial data structures
The design and analysis of spatial data structures
Cutting hyperplanes for divide-and-conquer
Discrete & Computational Geometry
The exact fitting problem in higher dimensions
Computational Geometry: Theory and Applications
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
New Lower Bounds for Convex Hull Problems in Odd Dimensions
SIAM Journal on Computing
Data mining: concepts and techniques
Data mining: concepts and techniques
Design of Dynamic Data Structures
Design of Dynamic Data Structures
Introduction to Algorithms
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Analyzing Relative Motion within Groups of Trackable Moving Point Objects
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
On approximating the depth and related problems
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Computing longest duration flocks in trajectory data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Computational Geometry: Theory and Applications
Trajectories Mining for Traffic Condition Renewing
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Detecting Regular Visit Patterns
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Detecting single file movement
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Graph Drawing
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Detecting areas visited regularly
COCOON'10 Proceedings of the 16th annual international conference on Computing and combinatorics
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Unsupervised trajectory sampling
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Implementing a qualitative calculus to analyse moving point objects
Expert Systems with Applications: An International Journal
Interpreting motion events of pairs of moving objects
Geoinformatica
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Mining pixel evolutions in satellite image time series for agricultural monitoring
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Scalable Detection of Spatiotemporal Encounters in Historical Movement Data
Computer Graphics Forum
Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
Algorithms for hotspot computation on trajectory data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
WADS'13 Proceedings of the 13th international conference on Algorithms and Data Structures
Visualizing interchange patterns in massive movement data
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Moving point object data can be analyzed through the discovery of patterns in trajectories. We consider the computational efficiency of detecting four such spatio-temporal patterns, namely flock, leadership, convergence, and encounter, as defined by Laube et al., Finding REMO--detecting relative motion patterns in geospatial lifelines, 201---214, (2004). These patterns are large enough subgroups of the moving point objects that exhibit similar movement in the sense of direction, heading for the same location, and/or proximity. By the use of techniques from computational geometry, including approximation algorithms, we improve the running time bounds of existing algorithms to detect these patterns.