A combination of decision tree learning and clustering for data classification

Abstract

In this paper, we present a new classification algorithm which is a combination of decision tree learning and clustering called Tree Bagging and Weighted Clustering (TBWC). The TBWC algorithm was developed to enhance a classification performance of a clustering algorithm. In the experiments, five datasets were used to evaluate the predictive performance. The experimental results show that the TBWC algorithm yields the highest accuracies when compared with decision tree learning and clustering for all datasets. In addition, this algorithm can improve the predictive performance especially for multi-class datasets which can increase the accuracy up to 36.67%. Finally, it can reduce attributes up to 59.82%.