Research And Application On Decision Tree In Data Mining

Data mining, also called as KDD(Knowledge Discover in Database),is an advanced process, in which we can pick up many trustful, novel, useful and readable patterns from very large amounts of data. Classification is an important problem in data mining. Classification now has been successfully applied to wide range of application areas. Many different techniques have been proposed for classification, decision tree classifiers have found the widest applicability in largescale data mining environments. The decision tree's formulation process is according to the inspiring rule. Decision tree classifier as one type of classifier is a flow-chart-like tree structure, where each internal node denotes a test on an attribute (attribute value), each branch represents an outcome of the test, and each leaf node represents a class. The method that a decision tree model is used to classify a record is to find a path that from root to leaf by measuring the attributes test, and the attribute on the leaf indicates to which the record belongs. The ID3 and C4.5 algorithms based on the information theory and the CART, SLIQ and PUBLIC methods based on the lowest GINI index are very common in the decision tree's building.This paper based on actual development of epidemic prevention and child immunity management system, begin with the practical application, several algorithms of decision tree classification have been investigated, advanced their insufficiency and improved the ID3 algorithm then implements the decision support module, the whole system also implements an interface with the countrywide monitor of child inoculate information management system.The paper discussed, compared and analyzed several decision tree classification algorithms, and we found a new application background for classification for further research. This system is at the stage of prime research and trying out, still there are lots of work about making the system more perfect so as to meet the users' need.