Improvement And Application Algorithm Based On Decision Tree

With the extensive application of Data Mining (DM), it's more and more important to get correlative knowledge or rule from data, so there are all kinds of technologies about Data Mining to be put forward in the latest decades in order to solve this problem. Decision tree algorithm is one of the core technique algorithms of DM. it's widely used in commerce. In decision tree algorithm, the famous one is ID3 algorithm, which was presented by Quinlan in 1986. It is not an algorithm increasing by degrees, and it uses information entropy as a standard to select attribute. But this algorithm has two disadvantages: one is that is easy to select those attributes whose values is more, while attributes whose values is more are not always the best; the other is bad at learning simple logic expressions. To solve this problem, this paper introduces new algorithm to improve ID3 algorithm. Comparing with the decision tree built by ID3 algorithm and new algorithm with the same example, we can know the new algorithm is better. Because the application of ID3 algorithm and improved algorithms lack of object-oriented method, so according to the object-oriented method and using C# to actualize ID3 algorithm and improved algorithm. The present study intends to set up an integrated system of decision tree construction, and make use of this system to classify the equipment administration in a coal equipment administration decision-making system. In the rules extracting phase we can easily covert the model to the classification IF-THEN rules.