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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/11931

Title: Coupled Heterogeneous Association Rule Mining (CHARM): Application toward Inference of Modulatory Climate Relationships
Authors: . Gonzalez, Doel L
Pendse, Saurabh V.
Padmanabhan, Kanchana
Angus∗, Michael P.
Tetteh∗, Isaac K.
Srinivas∗†, Shashank
and Ect....al
Keywords: —association rules
; climate;
data coupling;
; discovery
Issue Date: 2013
Publisher: IEEE
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
URI: http://hdl.handle.net/123456789/11931
Appears in Collections:College of Science

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