中国农业科学2011，Vol.44Issue(23)：4833-4840,8.DOI:10.3864/j.issn.0578-1752.2011.23.009
聚类、粗糙集与决策树的组合算法在地力评价中的应用
Applied Research of Combinatorial Algorithm of Clustering, Rough Set and Decision Tree Method in Productivity Evaluation
摘要
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.关键词
聚类/粗糙集/决策树/土壤评价/地力等级Key words
clustering/ rough set/ decision tree/ soil evaluation/ productivity grade引用本文复制引用
陈桂芬,马丽,董玮,辛敏刚..聚类、粗糙集与决策树的组合算法在地力评价中的应用[J].中国农业科学,2011,44(23):4833-4840,8.基金项目
国家"863"计划项目(2006AA 10A309,2006AA102271)、国家星火计划项目(2008GA661 003) （2006AA 10A309,2006AA102271）
