The Research of Meteorological Data Mining Using Discrete Bayesian Networks Classifier Based on Hadoop

DOI

10.2991/icecee-15.2015.189How to use a DOI?

Keywords

Bayesian Networks; Predictive Ability; Classified Prediction; Data Mining; Hadoop

Abstract

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 2015 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  - 10.2991/icecee-15.2015.189
ID  - Zhang2015/06
ER  -