This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2012 IEEE 12th International Conference on Data Mining Workshops
Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
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.
Index Terms:
Data mining,Shape,Trajectory,Indexes,Atmospheric measurements,Particle measurements,Extraterrestrial measurements,spatio-temporal co-occurring patterns,evolving spatio-temporal events,extended spatial representations
Citation:
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, pp.805-812, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
Usage of this product signifies your acceptance of the Terms of Use.