Multi-variable decision tree construction algorithm based on rough set and entropy
基于粗集和熵的多变量决策树的构造算法

Multi-variable decision tree is effectively used in the classification of data mining.The key to building lies in the reasonable choice of attributes combination based on the interconnection between attributes.The traditional method is achieved by the relative core of attributes,which is incomplete.A new alternation algorithm was offered.In this algorithm,the number of attributes contained by each node was restricted,and then,attributes combination was selected according to the redefined attribute dependability and the distance function based on condition entropy.In the end,an example was given to show that the method decreases the height of tree,and increases the explanation of classification.