A Generalized Decision Tree Using Relevance Analysis to Evaluate Condition Attributes
Junjie Chen · Journal of Taiyuan University of Technology · 2006
Efficiency and scalability are fundamental issues concerning data mining in large da- tabases.The decision tree is an important classifier in data mining.In this paper a decision tree classifier based on relevance analysis is proposed after discussing traditional algorithms.The main idea is to compact the training data and evaluate condition attributes with correlations,and made pruning and optimization process simplified in order to get high accuracy and fast classifying speed,which leads to efficient,high—quality,multiple—level classification of large amounts of data.The accuracy of the algorithm was proven and the complexity of time was analyzed in the paper,too.