Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Density biased sampling: an improved method for data mining and clustering
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Indexed-based density biased sampling for clustering applications
Data & Knowledge Engineering
Global distance-based segmentation of trajectories
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Time-focused clustering of trajectories of moving objects
Journal of Intelligent Information Systems
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Visual analytics tools for analysis of movement data
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Adaptive spherical Gaussian kernel in sparse Bayesian learning framework for nonlinear regression
Expert Systems with Applications: An International Journal
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Trajectory Voting and Classification Based on Spatiotemporal Similarity in Moving Object Databases
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Clustering Trajectories of Moving Objects in an Uncertain World
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Discovering spatio-temporal causal interactions in traffic data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 15th International Conference on Extending Database Technology
iSampling: framework for developing sampling methods considering user's interest
Proceedings of the 21st ACM international conference on Information and knowledge management
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A novel methodology for efficiently sampling Trajectory Databases (TD) for mobility data mining purposes is presented. In particular, a three-step unsupervised trajectory sampling methodology is proposed, that initially adopts a symbolic vector representation of a trajectory which, using a similarity-based voting technique, is transformed to a continuous function that describes the representativeness of the trajectory in the TD. This vector representation is then relaxed by a merging algorithm, which identifies the maximal representative portions of each trajectory, at the same time preserving the space-time mobility pattern of the trajectory. Finally, a novel sampling algorithm operating on the previous representation is proposed, allowing us to select a subset of a TD in an unsupervised way encapsulating the behavior (in terms of mobility patterns) of the original TD. An experimental evaluation over synthetic and real TD demonstrates the efficiency and effectiveness of our approach.