摘要
采用粗集理论求得以决策属性(树高)所对应的条件属性的最小简约集,并使用Apriori算法提取决策属性和条件属性的对应关系建立树高模型。与传统统计方法建立的树高模型相比,该模型具有较优的有效性和可行性。
To use rough set theory and obtain the minimum simple set of condition attributes that corresponding to the decision-making attributes（height）.The correspondence between the decision and conditions attributes was extracted using Apriori algorithm to establish the tree height model.The result of the former model has optimum effectiveness and feasibility comparing with that of traditional statistical methods tree height models.
出处
《安徽农业科学》
CAS
北大核心
2010年第16期8771-8774,共4页
Journal of Anhui Agricultural Sciences
关键词
数据挖掘
粗糙集
树高模型
Data mining
Rough set
Tree height model
