A Data Pre-processing Method Using Association Rules of Attributes for Improving Decision Tree

Abstract

One of the approaches to generate a good decision tree is preprocessing the data to improve its description. There are many researches on data pre-processing such as attributes generation and attributes selection methods. However, most of them are based on logic programming so that it takes much run time. Additionally, some of them need a priori knowledge. These are disadvantage for the data mining. We propose a novel data driven approach that knowledge on the relevance of attributes are generated as association rules from the data, so a priori knowledge is not necessary. In this paper, we present the method and clarify its feature. The effectiveness of our method as data mining one is evaluated through experiments.