Scientia Agricultura Sinica ›› 2011, Vol. 44 ›› Issue (23): 4833-4840.doi: 10.3864/j.issn.0578-1752.2011.23.009

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

Applied Research of Combinatorial Algorithm of Clustering，Rough Set and Decision Tree Method in Productivity Evaluation

 CHEN  Gui-Fen, MA  Li, DONG  Wei, XIN  Min-Gang   

  1. 1.吉林农业大学信息技术学院，长春 130118
    2.吉林省农安县农业技术推广总站，吉林农安 130200
  • Received:2010-07-26 Online:2011-12-01 Published:2011-06-14

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Abstract: 【Objective】 Fertility evaluation method has a certain subjective and less considers the dependence relation among soil attributes. This paper is aimed to seek a new method of productivity evaluation by data mining method. 【Method】 Based on Nong’an cultivated land survey data, the paper used optimization algorithm of K-means clustering method, Johnson rough set attribute reduction algorithm and C4.5 decision tree algorithm to evaluate the productivity grade. 【Result】 The best learning samples are obtained by using K-means clustering method. Rough sets are used in soil attribute reduction, and 7 soil redundant attributes are removed. The decision tree model has 317 nodes and 159 leaf nodes, extracts 159 rules, model accuracy is 82.08%. The decision tree node number decreased by 41.62% compared with no-clustering and no-reduction approaches. 【Conclusion】 Using the combination algorithm, while the accuracy of the model is ensured, the algorithm time and space complexity are reduced and the mining efficiency is improved.

Key words: clustering, rough set, decision tree, soil evaluation, productivity grade

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