Decision tree algorithm using archetype Abstraction and attribute classification value
基于原型抽象和分类价值量的决策树算法

In order to overcome the shortcomings of decision tree algorithm in large data sets, this paper proposed a novel decision tree algorithm based on rough set. The algorithm put forward a method based on representative instance for archetype abstraction, which extracted representative instances from original data set as abstraction archetype and decreased the number of instances and irrelevant attributes, hence it could deal with large data set. Simultaneity, the algorithm took attribute classification value as a heuristic measure for choosing attribute, which synthetically calculated contribution of an attribute for classification. It principally considered the dependency between attributes or relationship between instances and the classification. Mining experiments show that it can obtain higher accuracy and smaller size of decision tree than other algorithms, which make it more excellent for large data sets.