Paper Info
Title
Spatiotemporal Frequent Pattern Mining on Solar Data: Current Algorithms and Future Directions.
Abstract
In this paper, we present the current work and future directions on spatiotemporal frequent pattern mining algorithms for mining solar data. The current spatiotemporal pattern mining algorithms focus on spatiotemporal co-occurrence patterns. We reveal four types of spatiotemporal concepts that can be mined from solar data: event sequences, periodicity, spatiotemporal convergence and propagation. Throughout the paper, we exhibit examples of these concepts in the solar physics domain, and present related algorithms and the challenges of mining these concepts from solar data.
Year
DOI
Venue
2015
10.1109/ICDMW.2015.10
ICDM Workshops
Keywords
Field
DocType
Solar data mining,frequent pattern mining,spatiotemporal data mining
Convergence (routing),Evolution biology,Data mining,Computer science,Solar physics,Algorithm,Artificial intelligence,Machine learning,Trajectory,Spatiotemporal pattern
Conference
Citations 
PageRank 
References 
6
0.46
33
Authors
2
Name
Order
Citations
PageRank
Berkay Aydin14010.75
Rafal A. Angryk227145.56