Development of a Data Mining Application for Agriculture Based on Bayesian Networks

  • Jiejun Huang
  • Yanbin Yuan
  • Wei Cui
  • Yunjun Zhan
Part of the The International Federation for Information Processing book series (IFIPAICT, volume 258)

Data mining is a process by which the data can be analyzed so as to generate useful knowledge. It aims to use existing data to invent new facts and to uncover new relationships previously unknown even to experts. Bayesian network is a powerful tool for dealing with uncertainties, and has a widespread use in the area of data mining. In this paper, we focus on development of a data mining application for agriculture based on Bayesian networks. Let features (or objects) as variables or the nodes in Bayesian network, let directed edges present the relationships between features, and the relevancy intensity can be regarded as confidence between the variables. Accordingly, it can find the relationships in the agricultural data by learning a Bayesian network. After defining the domain variables and data preparation, we construct a model for agricultural application based on Bayesian network learning method. The experimental results indicate that the proposed method is feasible and efficient, and it is a promising approach for data mining in agricultural data.

Keywords

data mining Bayesian network model agriculture 

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Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Jiejun Huang
    • 1
  • Yanbin Yuan
    • 1
  • Wei Cui
    • 1
  • Yunjun Zhan
    • 1
  1. 1.School of Resource and Environmental EngineeringWuhan University of TechnologyChina

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