Clustering vessel trajectories with alignment kernels under trajectory compression
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Unsupervised trajectory sampling
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Geotagging in multimedia and computer vision--a survey
Multimedia Tools and Applications
Mining spatial trajectories using non-parametric density functions
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Deriving implicit indoor scene structure with path analysis
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
Visually exploring movement data via similarity-based analysis
Journal of Intelligent Information Systems
Constructing popular routes from uncertain trajectories
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
NSS'12 Proceedings of the 6th international conference on Network and System Security
Map-matched trajectory compression
Journal of Systems and Software
A Hybrid Grid-based Method for Mining Arbitrary Regions-of-Interest from Trajectories
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
The influence of global constraints on similarity measures for time-series databases
Knowledge-Based Systems
Vector field k-means: clustering trajectories by fitting multiple vector fields
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
Hi-index | 0.00 |
Mining Trajectory Databases (TD) has recently gained great interest due to the popularity of tracking devices. On the other hand, the inherent presence of uncertainty in TD (e.g., due to GPS errors) has not been taken yet into account during the mining process. In this paper, we study the effect of uncertainty in TD clustering and introduce a three-step approach to deal with it. First, we propose an intuitionistic point vector representation of trajectories that encompasses the underlying uncertainty and introduce an effective distance metric to cope with uncertainty. Second, we devise CenTra, a novel algorithm which tackles the problem of discovering the Centroid Trajectory of a group of movements. Third, we propose a variant of the Fuzzy C-Means (FCM) clustering algorithm, which embodies CenTra at its update procedure. The experimental evaluation over real world TD demonstrates the efficiency and effectiveness of our approach.