A rule extraction algorithm based on attribute importance

Classification algorithm is a kind of important technology in data mining, and the most commonly used is decision tree learning. In the process of constructing a decision tree, the selecting criteria of splitting attributes will directly affect the classification results. And the attribute selection of the traditional decision tree algorithm is based on information theory. In this paper, by combining with rough sets theory, we propose a new rules extraction algorithm based on attributes importance and dependence. Compared with the other algorithm, our algorithm is simple, by which we can obtain comprehensive rules without redundancy, and it also gives rule mining process with higher reliability.