Efficiently detecting clusters of mobile objects in the presence of dense noise
Proceedings of the 2010 ACM Symposium on Applied Computing
Spatio-temporal clustering of road network data
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Tracing evolving clusters by subspace and value similarity
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Tracing Evolving Subspace Clusters in Temporal Climate Data
Data Mining and Knowledge Discovery
Nesting the earth mover's distance for effective cluster tracing
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Adaptive evolutionary clustering
Data Mining and Knowledge Discovery
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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This paper addresses the problem of detecting and tracking moving clusters in spatio-temporal data sets. Spatio-temporal data sets contain data elements that move in space over time. Traditional data clustering algorithms work well on static data sets that contain well separated clusters. When traditional techniques are applied to spatio-temporal data they breakdown when the moving data elements intersect the space occupied by elements from another cluster. The goal of this work is to improve the accuracy of traditional data clustering algorithms on spatio-temporal data sets. Many clustering algorithms create clusters based on the distance between the elements. We extend this distance measure to be a function of the position history of the elements. We show through a series of experiments that the use of the history based distance measures greatly improves the performance of existing data clustering algorithms on spatio-temporal data sets. In random data sets we achieve up to a 90% improvement in cluster accuracy. To evaluate the clustering algorithms we created 102 spatio-temporal data sets. We also defined a set of metrics that are used to evaluate the performance of the clustering algorithms on the spatio-temporal data sets.