An Idea of Improvement Decision Tree Learning Using Cluster Analysis
Saori Amanuma, Masaki Kurematsu, Hamido Fujita · Frontiers in artificial intelligence and applications · 2012
In this paper, we proposed an idea of improvement of a decision tree learning algorithm using cluster analysis. We classify data set based on two relations. One is the relation between each class and each attribute and the other is the relation between attributes. First relation is used in a traditional decision tree algorithm and second relation is used in cluster analysis. Using second relation is our point in this approach. In order to evaluate our approach, we did an experiment using data set in machine learning repositories. Experimental result show the possibility that our approach is better than a traditional decision tree learning algorithm.