@article{gonzalez_angus_tetteh_bello_padmanabhan_pendse_srinivas_yu_semazzi_kumar_et al._2015, title={On the data-driven inference of modulatory networks in climate science: an application to West African rainfall}, volume={22}, ISSN={["1607-7946"]}, DOI={10.5194/npg-22-33-2015}, abstractNote={Abstract. Decades of hypothesis-driven and/or first-principles research have been applied towards the discovery and explanation of the mechanisms that drive climate phenomena, such as western African Sahel summer rainfall~variability. Although connections between various climate factors have been theorized, not all of the key relationships are fully understood. We propose a data-driven approach to identify candidate players in this climate system, which can help explain underlying mechanisms and/or even suggest new relationships, to facilitate building a more comprehensive and predictive model of the modulatory relationships influencing a climate phenomenon of interest. 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 modulate Sahel rainfall. These relationships fall into two categories: well-known associations from prior climate knowledge, such as the relationship with the El Niño–Southern Oscillation (ENSO) and putative links, such as North Atlantic Oscillation, that invite further research.
                    }, number={1}, journal={NONLINEAR PROCESSES IN GEOPHYSICS}, author={Gonzalez, D. L., II and Angus, M. P. and Tetteh, I. K. and Bello, G. A. and Padmanabhan, K. and Pendse, S. V. and Srinivas, S. and Yu, J. and Semazzi, F. and Kumar, V. and et al.}, year={2015}, pages={33–46} }
 @article{gonzalez_pendse_padmanabhan_angus_tetteh_srinivas_villanes_semazzi_kumar_samatova_2013, title={Coupled Heterogeneous Association Rule Mining (CHARM): Application toward Inference of Modulatory Climate Relationships}, ISSN={["1550-4786"]}, DOI={10.1109/icdm.2013.142}, abstractNote={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.}, journal={2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)}, author={Gonzalez, Doel L., II and Pendse, Saurabh V. and Padmanabhan, Kanchana and Angus, Michael P. and Tetteh, Isaac K. and Srinivas, Shashank and Villanes, Andrea and Semazzi, Fredrick and Kumar, Vipin and Samatova, Nagiza F.}, year={2013}, pages={1055–1060} }
 @article{chen_hendrix_guan_tetteh_choudhary_semazzi_samatova_2013, title={Discovery of extreme events-related communities in contrasting groups of physical system networks}, volume={27}, ISSN={["1573-756X"]}, DOI={10.1007/s10618-012-0289-3}, abstractNote={The latent behavior of a physical system that can exhibit extreme events such as hurricanes or rainfalls, is complex. Recently, a very promising means for studying complex systems has emerged through the concept of complex networks. Networks representing relationships between individual objects usually exhibit community dynamics. Conventional community detection methods mainly focus on either mining frequent subgraphs in a network or detecting stable communities in time-varying networks. In this paper, we formulate a novel problem—detection of predictive and phase-biased communities in contrasting groups of networks, and propose an efficient and effective machine learning solution for finding such anomalous communities. We build different groups of networks corresponding to different system's phases, such as higher or low hurricane activity, discover phase-related system components as seeds to help bound the search space of community generation in each network, and use the proposed contrast-based technique to identify the changing communities across different groups. The detected anomalous communities are hypothesized (1) to play an important role in defining the target system's state(s) and (2) to improve the predictive skill of the system's states when used collectively in the ensemble of predictive models. When tested on the two important extreme event problems—identification of tropical cyclone-related and of African Sahel rainfall-related climate indices—our algorithm demonstrated the superior performance in terms of various skill and robustness metrics, including 8–16 % accuracy increase, as well as physical interpretability of detected communities. The experimental results also show the efficiency of our algorithm on synthetic datasets.}, number={2}, journal={DATA MINING AND KNOWLEDGE DISCOVERY}, author={Chen, Zhengzhang and Hendrix, William and Guan, Hang and Tetteh, Isaac K. and Choudhary, Alok and Semazzi, Fredrick and Samatova, Nagiza F.}, year={2013}, month={Sep}, pages={225–258} }
 @article{gonzalez_chen_tetteh_pansombut_semazzi_kumar_melechko_samatova_2012, title={Hierarchical Classifier-Regression Ensemble for Multi-Phase Non-Linear Dynamic System Response Prediction: Application to Climate Analysis}, ISBN={["978-1-4673-5164-5"]}, ISSN={["2375-9232"]}, DOI={10.1109/icdmw.2012.133}, abstractNote={A dynamic physical system often undergoes phase transitions in response to fluctuations induced on system parameters. For example, hurricane activity is the climate system's response initiated by a liquid-vapor phase transition associated with non-linearly coupled fluctuations in the ocean and the atmosphere. Because our quantitative knowledge about highly non-linear dynamic systems is very meager, scientists often resort to linear regression techniques such as Least Absolute Deviation (LAD) to learn the non-linear system's response (e.g., hurricane activity) from observed or simulated system's parameters (e.g., temperature, precipitable water, pressure). While insightful, such models still offer limited predictability, and alternatives intended to capture non-linear behaviors such as Stepwise Regression are often controversial in nature. In this paper, we hypothesize that one of the primary reasons for lack of predictability is the treatment of an inherently multi-phase system as being phase less. To bridge this gap, we propose a hybrid approach that first predicts the phase the system is in, and then estimates the magnitude of the system's response using the regression model optimized for this phase. Our approach is designed for systems that could be characterized by multi-variate spatio-temporal data from observations, simulations, or both.}, journal={12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012)}, author={Gonzalez, Doel L., II and Chen, Zhengzhang and Tetteh, Isaac K. and Pansombut, Tatdow and Semazzi, Fredrick and Kumar, Vipin and Melechko, Anatoli and Samatova, Nagiza F.}, year={2012}, pages={781–788} }