Data mining: concepts and techniques
Data mining: concepts and techniques
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Spatio-Temporal Databases
Efficient detection of motion patterns in spatio-temporal data sets
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
A Joinless Approach for Mining Spatial Colocation Patterns
IEEE Transactions on Knowledge and Data Engineering
Sustained Emerging Spatio-Temporal Co-occurrence Pattern Mining: A Summary of Results
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Mixed-Drove Spatio-Temporal Co-occurence Pattern Mining: A Summary of Results
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Correlation analysis of spatial time series datasets: a filter-and-refine approach
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and 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
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Spatio-temporal co-occurrence patterns (STCOPs) represent subsets of features that are located together in space and time. Mining such patterns is important for many spatio-temporal application domains. However, a co-occurrence analysis across multiple spatio-temporal datasets is computationally expensive when the dimension of the time series and number of locations in the spaces are large. In this paper, we first defined STCOPs and the STCOPs mining problem. We proposed a monotonic composite measure, which is the composition of the spatial prevalence and temporal prevalence measures. A novel and computationally efficient algorithm, Costcop+, is presented by applying the composite measure. We proved that the proposed algorithm is correct and complete in finding STCOPs. Using a real dataset, the experiments illustrate that the algorithm is efficient.