Accuracy Improvement of C4.5 using K means Clustering

Data mining is used to extract useful information from the vast amount of data. Classification is one of the techniques of data mining which will be used to predict the target attribute accurately from the knowledge it gained from the training data. There are various classifiers which among decision tree is very simple and most effective method. Accuracy of a classifier is how well it predicts the target attribute of test data correctly from the knowledge gained from the training set. In this paper, classification accuracy of C4.5 is improved with K means Clustering and adding the tested data dynamically to the training set. This improved C4.5 predicts the target variable with higher accuracy level with the help of K means clustering which is used to discretize the continuous data.