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Issue No.04 - July-Aug. (2014 vol.29)
pp: 10-17
Yuanchun Zhou , Chinese Academy of Sciences
Mingjie Tang , Chinese Academy of Sciences
Weike Pan , Hong Kong Baptist University
Jinyan Li , University of Technology, Sydney
Weihang Wang , Chinese Academy of Sciences
Jing Shao , Chinese Academy of Sciences
Liang Wu , Chinese Academy of Sciences
Jianhui Li , Chinese Academy of Sciences
Qiang Yang , Huawei Noah's Ark Lab
Baoping Yan , Chinese Academy of Sciences
ABSTRACT
Advanced satellite tracking technologies have collected huge amounts of wild bird migration data. Biologists use these data to understand dynamic migration patterns, study correlations between habitats, and predict global spreading trends of avian influenza. The research discussed here transforms the biological problem into a machine learning problem by converting wild bird migratory paths into graphs. H5N1 outbreak prediction is achieved by discovering weighted closed cliques from the graphs using the mining algorithm High-wEight cLosed cliquE miNing (HELEN). The learning algorithm HELEN-p then predicts potential H5N1 outbreaks at habitats. This prediction method is more accurate than traditional methods used on a migration dataset obtained through a real satellite bird-tracking system. Empirical analysis shows that H5N1 spreads in a manner of high-weight closed cliques and frequent cliques.
INDEX TERMS
Diseases, Target tracking, Prediction algorithms, Databases, Data mining, Birds, Algorithm design and analysis, Satellite navigation systems, China, Machine learning,intelligent systems, computational sustainability, bird flu prediction, wild bird migration data mining, H5N1 prediction in Qinghai Lake, China, machine learning, graph mining
CITATION
Yuanchun Zhou, Mingjie Tang, Weike Pan, Jinyan Li, Weihang Wang, Jing Shao, Liang Wu, Jianhui Li, Qiang Yang, Baoping Yan, "Bird Flu Outbreak Prediction via Satellite Tracking", IEEE Intelligent Systems, vol.29, no. 4, pp. 10-17, July-Aug. 2014, doi:10.1109/MIS.2013.38
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