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2013 IEEE 13th International Conference on Data Mining Workshops (2012)
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", 2013 IEEE 13th International Conference on Data Mining Workshops, vol. 00, no. , pp. 805-812, 2012, doi:10.1109/ICDMW.2012.130
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