Decision Tree Classification Algorithm And Its Application

The Data Mining technique is widely applied and it becomes more and more mature along with the discussion and research about Data Mining theory. The decision tree method is important one that is used to data classification and forecast domain in many Data Mining techniques and methods. It is an inductive arithmetic which bases instances, and it can find the classification rule through illation from immethodical and ruleless instances. Then we can make use of the rules to forecast unknown data .ID3 algorithm is the most frequently-used achieved method in decision tree constructors, and it is widely applied in data classification and forecast domain. But we find lots of defect about ID3 in practical application. So the paper researches the defect of ID3 and improved algorithm, and gives the rational prioritization scheme to perfect the ID3. The prioritization scheme comprises two aspects as follows:Firstly, we predigest the heuristic function of ID3. The paper approximately derives the information gain formulae to remove the logarithm operation, and we derive the simplified heuristic function that is the same with several sorts and possesses universal property and universality. The new shortcut calculation of ID3 selects the attribute whose information gain is the least as attributetest, and avoids logarithm operation when calculating information gain. So the shortcut calculation of ID3 decreases calculated amount and improves the execution efficiency of arithmetic.Secondly, the paper introduces the weight function to overcome the problem of variety bias. The weight function weighs the relation between number of attribute value and information gain through assigning different weights for different attributes, then we can derive the new standard of Choosing Attributes. After instance analysis and algorithm comparison, the selected attributetest is more logical through modified ID3. Then the rules from decision tree more answer for the needs of people.Lastly, the paper realizes the application of ID3 optimization algorithm in decision problem of students'renewal tuition through an instance. According to the application process, we integrate students'essential information table and feedback table into new data set which is used to ID3 optimization algorithm. Finally, we derive decision tree and distill rules from decision tree. According to these rules, company Manager could more exactly make judgement and decision. And these rules could improve the benefit of company.