Coupled heterogeneous association rule mining (CHARM): Application toward inference of modulatory climate relationships

Doel L. Gonzalez, Saurabh V. Pendse, Kanchana Padmanabhan, Michael P. Angus, Isaac K. Tetteh, Shashank Srinivas, Andrea Villanes, Fredrick Semazzi, Vipin Kumar, Nagiza F. Samatova

Research output: Contribution to journalConference article

4 Scopus citations

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.

Original languageEnglish (US)
Article number6729597
Pages (from-to)1055-1060
Number of pages6
JournalProceedings - IEEE International Conference on Data Mining, ICDM
DOIs
StatePublished - Dec 1 2013
Event13th IEEE International Conference on Data Mining, ICDM 2013 - Dallas, TX, United States
Duration: Dec 7 2013Dec 10 2013

Keywords

  • association rules
  • climate
  • data coupling
  • knowledge discovery

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    Gonzalez, D. L., Pendse, S. V., Padmanabhan, K., Angus, M. P., Tetteh, I. K., Srinivas, S., Villanes, A., Semazzi, F., Kumar, V., & Samatova, N. F. (2013). Coupled heterogeneous association rule mining (CHARM): Application toward inference of modulatory climate relationships. Proceedings - IEEE International Conference on Data Mining, ICDM, 1055-1060. [6729597]. https://doi.org/10.1109/ICDM.2013.142