Articles | Volume 22, issue 1
https://doi.org/10.5194/npg-22-33-2015
https://doi.org/10.5194/npg-22-33-2015
Research article
 | 
13 Jan 2015
Research article |  | 13 Jan 2015

On the data-driven inference of modulatory networks in climate science: an application to West African rainfall

D. L. González II, M. P. Angus, I. K. Tetteh, G. A. Bello, K. Padmanabhan, S. V. Pendse, S. Srinivas, J. Yu, F. Semazzi, V. Kumar, and N. F. Samatova

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Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques
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Nonlin. Processes Geophys., 21, 777–795, https://doi.org/10.5194/npg-21-777-2014,https://doi.org/10.5194/npg-21-777-2014, 2014

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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
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Cited articles

Agrawal, R. and Srikant, R.: Fast algorithms for mining association rules in large databases, in: VLDB 1994, edited by: Bocca, J., Jarke, M., and Zaniolo, C., 487–499, 1994.
Agrawal, R., Imieli\'nski, T., and Swami, A.: Mining association rules between sets of items in large databases, Sigmod Record, 22, 207–216, 1993.
Bailey, T. L. and Gribskov, M.: Combining evidence using p-values: application to sequence homology searches, Bioinformatics, 14, 48–54, 1998.
Borboudakis, G., Triantafilou, S., Lagani, V., and Tsamardinos, I.: A constraint-based approach to incorporate prior knowledge in causal models, in: ESANN'2011, 2011.
Bühlmann, P.: Causal statistical inference in high dimensions, Math. Meth. of OR, 77, 357–370, 2013.
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Short summary
We applied coupled heterogeneous association rule mining (CHARM), Lasso multivariate regression, and dynamic Bayesian networks to find relationships within a complex system, and explored means with which to obtain a consensus result from the application of such varied methodologies. Using this fusion of approaches, we identified relationships among climate factors that fall into two categories: well-known associations from prior knowledge, and putative links that invite further research.