  Decision tree is an important method for both induction research and data
mining, which is mainly used for model classification and prediction. ID3
algorithm is the most widely used algorithm in the decision tree so far. In
this paper, the shortcoming of ID3's inclining to choose attributes with many
values is discussed, and then a new decision tree algorithm which is improved
version of ID3. In our proposed algorithm attributes are divided into groups
and then we apply the selection measure 5 for these groups. If information gain
is not good then again divide attributes values into groups. These steps are
done until we get good classification/misclassification ratio. The proposed
algorithms classify the data sets more accurately and efficiently.
