计算机科学技术学报(英文版)
計算機科學技術學報(英文版)
계산궤과학기술학보(영문판)
COMPUTER JOURNAL OF SCIENCE AND TECHNOLOGY
2002年
1期
18-27
,共10页
周傲英%钱卫宁%钱海蕾%金文
週傲英%錢衛寧%錢海蕾%金文
주오영%전위저%전해뢰%금문
data mining%classification%over branching%decision tree%frequent pattern
Classification is an important technique in data mining. The decision trees built by most of the existing classification algorithms commonly feature over-branching, which will lead to poor efficiency in the subsequent classification period. In this paper, we present a new value-oriented classification method, which aims at building accurately proper-sized decision trees while reducing over-branching as much as possible, based on the concepts of frequentpattern-node and exceptive-child-node. The experiments show that while using relevant analysis as pre-processing, our classification method, without loss of accuracy, can eliminate the over-branching greatly in decision trees more effectively and efficiently than other algorithms do.
