Paper Info
Title
Coupled Heterogeneous Association Rule Mining (Charm): Application Toward Inference Of Modulatory Climate Relationships
Abstract
The complex dynamic climate system often exhibits hierarchical modularity of its organization and function. Scientists have spent decades trying to discover and understand the driving mechanisms behind western African Sahel summer rainfall variability, mostly via hypothesis-driven and/or first-principles based research. Their work has furthered theory regarding the connections between various climate patterns, but the key relationships are still not fully understood. We present Coupled Heterogeneous Association Rule Mining (CHARM), a computationally efficient methodology that mines higher-order relationships between these subsystems' anomalous temporal phases with respect to their effect on the system's response. We apply this to climate science data, aiming to infer putative pathways/cascades of modulating events and the modulating signs that collectively define the network of pathways for the rainfall anomaly in the Sahel. Experimental results are consistent with fundamental theories of phenomena in climate science, especially physical processes that best describe sub-regional climate.
Year
DOI
Venue
2013
10.1109/ICDM.2013.142
2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)
Keywords
Field
DocType
association rules, climate, data coupling, discovery
Data mining,Climate science,Inference,Computer science,Climate pattern,Association rule learning,Knowledge extraction,Artificial intelligence,Modularity,Machine learning
Conference
ISSN
Citations 
PageRank 
1550-4786
2
0.40
References 
Authors
3
10
Name
Order
Citations
PageRank
Doel L. Gonzalez120.74
Saurabh V. Pendse2483.33
Kanchana Padmanabhan3152.74
Michael P. Angus441.12
Isaac K. Tetteh5182.80
Shashank Srinivas650.79
Andrea Villanes720.40
Fredrick H. M. Semazzi8233.42
Vipin Kumar94331385.81
Nagiza F. Samatova1086174.04