Coupled Heterogeneous Association Rule Mining (CHARM): Application Toward Inference of Modulatory Climate Relationships-Reference-Cited by-同舟云学术

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Coupled Heterogeneous Association Rule Mining (CHARM): Application Toward Inference of Modulatory Climate Relationships

Published:2013-12 Issue: Volume: Page:
ISSN:
Container-title:2013 IEEE 13th International Conference on Data Mining
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Author:

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

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/6724379/6729471/06729597.pdf?arnumber=6729597

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models;Journal of Hydrology;2017-05

2. Contrast Pattern Based Methods for Visualizing and Predicting Spatiotemporal Events;2015 IEEE International Conference on Data Mining Workshop (ICDMW);2015-11

3. A Hierarchical Pattern Learning Framework for Forecasting Extreme Weather Events;2015 IEEE International Conference on Data Mining;2015-11

4. On the data-driven inference of modulatory networks in climate science: an application to West African rainfall;Nonlinear Processes in Geophysics;2015-01-13

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