Using Decision Tree to Summarize Associative Classification Rules

• Main Authors: Tzu-hsuan Hung, 洪子軒
• Other Authors: 陳彥良
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/07372134788817702281

碩士 === 國立中央大學 === 資訊管理研究所 === 95 === Association rule mining is one of the most popular areas in data mining. It is to discover items that co-occur frequently within a set of transactions, and to discover rules based on these co-occurrence relations. Association rules have been adopted into classification problem for years (associative classification). However, once rules have been generated, their lacking of organization causes readability problem, i.e., it is difficult for user to analyze them and understand the domain. To resolve this weakness, our work presented two algorithms that can use decision tree to summarize associative classification rules. As a classification model, it connects the advantages of both associative classification and decision tree. On one hand, it is a more readable, compact, well-organized form and easier to use when compared to associative classification. On the other hand, it is more accurate than traditional TDIDT (abbreviated from Top-Down Induction of Decision Trees) classification algorithm.