Classifying Chinese Text Documents by Association rule

• Main Authors: Cho-Ming Lee, 李卓銘
• Other Authors: Lain-Jinn Huang
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/53570890388178573247

碩士 === 淡江大學 === 資訊工程學系碩士在職專班 === 95 === Use improved TFIDF to build weighting table. Thereby, the system computes the sum of weight of each document relative to each category. According to this way, we can classify the documents which haven’t been labeled. In this paper, we use improve TFIDF to calculate the keywords weight and then combine two words as a new word by association rule to help us increase the keywords. We exploit association rule technology to apply to the data mining miner. The features of weight table are input into the data mining miner and examined whether these rules sorted by confidence, support and the length of rule to save into rule base. It will make the classification more efficiency.