The Research of Meteorological Data Mining Using Discrete Bayesian Networks Classifier Based on Hadoop
Yongjun Zhang, Jing Sun
Available Online June 2015.
- https://doi.org/10.2991/icecee-15.2015.189How to use a DOI?
- Bayesian Networks; Predictive Ability; Classified Prediction; Data Mining; Hadoop
- The method of Native Bayesian classification data mining in weather forecast has some defects, such as there is not independent of each other between predictors, but a certain relevance which results in the decrease of prediction accuracy. This paper explores an improved algorithm which is based on the theory of discrete Bayesian Networks, and combines with Hadoop distributed file system and parallel processing programming models to predict rainfall. The experiments show that the improved algorithm not only makes the classification prediction more reliable but also improves the efficiency greatly. In addition, it provides a solution of huge amounts of data mining in the other fields.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Yongjun Zhang AU - Jing Sun PY - 2015/06 DA - 2015/06 TI - The Research of Meteorological Data Mining Using Discrete Bayesian Networks Classifier Based on Hadoop BT - 2015 2nd International Conference on Electrical, Computer Engineering and Electronics PB - Atlantis Press SP - 996 EP - 1001 SN - 2352-538X UR - https://doi.org/10.2991/icecee-15.2015.189 DO - https://doi.org/10.2991/icecee-15.2015.189 ID - Zhang2015/06 ER -