Abstract:
In many areas, large quantities of data are generated and collected everyday, such as supermarket, business, astronomy, geography, phone call records etc. These data arrive too fast to be analyzed or mined in time. The goal of this paper is to extract data easily and rapidly. The main task performed in this system is using decision tree induction methods to the given values of attributes of an unknown object to determine appropriate classification according to decision tree rules. Test attributes are selected on the basis of Information gain measure. In this paper, the classification of training data is proposed in which the resulting classifier is a decision tree induction. Tree method for classification is exploited to identify user’s data. The information gain measure method is used which computes the highest information gain of each attribute for the training data set.
