关键词: 数据挖掘, 分类, 决策树, 粗糙集, ID3, 熵

Abstract: Data mining is an important part of AI and classification is a kind of useful application.ID3 algorithm is a classical algorithm in data mining，the algorithm has the worse ability to resist noise.Through the research on variable precision rough set，the algorithm is improved by setting threshold value while calculating attributes’ entropy，in order to relax the restrictions while selecting attributes.After using the improved ID3 algorithm（VPID3），the interference of noise to classification could be reduced to a certain extent，this made result correspond to reality even more.Finally，the paper designs and realizes a classifier using VPID3 algorithm and do some experiments to check its performance.Extensive experiments with four different datasets have shown that our algorithm is more effective in dealing with noise data than ID3 algorithm.

Key words: data mining, classification, decision tree, rough set, ID3, entropy