期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2012
卷号：2
期号：4
出版社：S.S. Mishra
摘要：Many organizations have large quantities of data collected in various application areas. Classification of data is a major issue which leads less efficiency and scalability. In this paper, we developed a new method for decision tree for classification of data using a data structure called Peano Count Tree (P-tree) which enhances the efficiency and scalability. We apply Data Smoothing and Attribute Relevance techniques along with a classifier. Experimental results show that the P-tree method is significantly faster than existing classification methods, making it the preferred method for mining on data to be classified.
关键词：Decision Tree Induction; Data Mining; Classification; Data Smoothing; Attribute Relevance Data; Peano ;Count Trees.
