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|Title: ||Coupled Heterogeneous Association Rule Mining (CHARM): Application toward Inference of Modulatory Climate Relationships|
|Authors: ||. Gonzalez, Doel L|
Pendse, Saurabh V.
Angus∗, Michael P.
Tetteh∗, Isaac K.
|Keywords: ||—association rules|
|Issue Date: ||2013|
|Citation: ||2013 IEEE 13th International Conference on Data Mining|
|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 firstprinciples 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.|
|Description: ||An article published by 2013 IEEE 13th International Conference on Data Mining andavailable at DOI 10.1109/ICDM.2013.142|
|Appears in Collections:||College of Science|
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