A framework for integrating a decision tree learning algorithm and cluster analysis

Abstract

We proposed a modified decision tree learning algorithm to improve this algorithm in this paper. Our proposed approach classifies given data set by a traditional decision tree learning algorithm and cluster analysis and selects whichever is better according to information gain. In order to evaluate our approach, we did an experiment using program-generated data sets. We compared ID3 which is one of well-known decision tree learning algorithm to our approach about the recall ratio in this experiment. Experimental result shows the recall ratio of our approach is similar than the recall ratio of a traditional decision tree learning algorithm. Though we can not show the advantage of our approach according to the experiment, we show it is worth using cluster analysis to make a decision tree. In future, we have to evaluate our approach according to cross-validation method using big and complex data sets in order to say the advantage of our approach. We think our approach is not good for all data set, so we try to find the situation which our approach is better than other approaches according to the experimental results. In addition to, we have to show how to explain a decision tree by our approach to keep the readability of a decision tree.