BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
On the Generation of Time-Evolving Regional Data
Geoinformatica
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Toward Unsupervised Correlation Preserving Discretization
IEEE Transactions on Knowledge and Data Engineering
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Spatio-temporal data reduction with deterministic error bounds
The VLDB Journal — The International Journal on Very Large Data Bases
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Spatio-temporal frequent patterns discovered from historical trajectories of moving objects can provide important knowledge for location-based services. To address the problem of finding sequential patterns from spatio-temporal datasets, continuous values of spatial and temporal attributes should be discretized with the minimum loss of information. Since data carries spatio-temporal correlation among attributes, it should be preserved during discretization to derive accurate patterns. In this paper, we define the problem of discretizing spatio-temporal data and propose a discretization method preserving spatio-temporal correlations in the data. Using line simplification, our method first abstracts trajectories into approximations considering the distributions of input data and then clusters them into logical cells. We experimentally analyze the effectiveness of the proposed approach in reducing the size of data and improving efficiency of the mining processes.