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Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
ISBN: 978-1-4673-5164-5
pp: 805-812
Spatio-temporal co-occurring patterns represent subsets of event types that occur together in both space and time. In comparison to previous work in this field, we present a general framework to identify spatio-temporal co occurring patterns for continuously evolving spatio-temporal events that have polygon-like representations. We also propose a set of measures to identify spatio-temporal co-occurring patterns and propose an Apriori-based spatio-temporal co-occurrence mining algorithm to find prevalent spatio-temporal co-occurring patterns for extended spatial representations that evolve over time. We evaluate our framework on real-life data to demonstrate the effectiveness of our measures and the algorithm. We present results highlighting the importance of our measures in identifying spatio-temporal co-occurrence patterns.
Data mining, Shape, Trajectory, Indexes, Atmospheric measurements, Particle measurements, Extraterrestrial measurements, spatio-temporal co-occurring patterns, evolving spatio-temporal events, extended spatial representations
Karthik Ganesan Pillai, Rafal A. Angryk, Juan M. Banda, Michael A. Schuh, Tim Wylie, "Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 805-812, doi:10.1109/ICDMW.2012.130
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