Trajectory sampling for direct traffic observation
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
SVM selective sampling for ranking with application to data retrieval
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Efficiently learning the accuracy of labeling sources for selective sampling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Migration motif: a spatial - temporal pattern mining approach for financial markets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Spatiotemporal sampling for trajectory streams
Proceedings of the 2010 ACM Symposium on Applied Computing
Unsupervised trajectory sampling
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Passive Sampling for Regression
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Nearest neighbor search on moving object trajectories
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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Sampling is one of fundamental techniques for data preprocessing and mining. It helps to reduce computational costs and improve the mining quality. A sampling method is typically developed independently for a specific problem and for a specific user's interest, because it is hard to develop a method that is generalized across various user's interests. An absence of general framework for sampling makes it inefficient to develop or revise a sampling method as user's interest changes. This paper proposes a general framework, isampling, which facilitates a user developing sampling methods and easily modifying the user's sampling interest in the method. In the framework, a user explicitly describes her sampling interest into a graph model called interest model. Then, isampling automatically selects a sample set according to the model, which satisfies the user's interest. In order to demonstrate the effectiveness of our framework, we develop new trajectory sampling methods using our framework; trajectory sampling has been a challenging problem due to its high complexity of data and various user's interests. We demonstrate the flexibility of our framework by showing how easily trajectory samples of different interests can be generated within our framework.