Simulated annealing: theory and applications
Simulated annealing: theory and applications
Introduction to the IBM optimization subroutine library
IBM Systems Journal
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Scan Statistics: Methods and Applications
Scan Statistics: Methods and Applications
Algorithms for rapid outbreak detection: a research synthesis
Journal of Biomedical Informatics
Detection of emerging space-time clusters
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
The hunting of the bump: on maximizing statistical discrepancy
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Time-focused clustering of trajectories of moving objects
Journal of Intelligent Information Systems
Anomalous window discovery through scan statistics for linear intersecting paths (SSLIP)
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal-spectral data mining in anomaly detection for spectrum monitoring
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Regional behavior change detection via local spatial scan
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Annals of Mathematics and Artificial Intelligence
Spatio-temporal outlier detection in precipitation data
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
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Detection of space-time clusters is an important function in various domains (e.g., epidemiology and public health). The pioneering work on the spatial scan statistic is often used as the basis to detect and evaluate such clusters. State-of-the-art systems based on this approach detect clusters with restrictive shapes that cannot model growth and shifts in location over time. We extend these methods significantly by using the flexible square pyramid shape to model such effects. A heuristic search method is developed to detect the most likely clusters using a randomized algorithm in combination with geometric shapes processing. The use of Monte Carlo methods in the original scan statistic formulation is continued in our work to address the multiple hypothesis testing issues. Our method is applied to a real data set on brain cancer occurrences over a 19 year period. The cluster detected by our method shows both growth and movement which could not have been modeled with the simpler cylindrical shapes used earlier. Our general framework can be extended quite easily to handle other flexible shapes for the space-time clusters.