Mammography Classification By an Association Rule-based Classifier

This paper proposes a new classification method based on association rule mining. This association rule-based classifier is experimented on a real dataset; a database of medical images. The system we propose consists of: a preprocessing phase, a phase for mining the resulted transactional database, and a final phase to organize the resulted association rules in a classification model. The experimental results show that the method performs well reaching over 80% in accuracy. Moreover, this paper illustrates, by comparison to other published research, how important the data cleaning phase is in building an accurate data mining architecture for image classification. KEY WORDS Mammography Mining, Image Classification, Document Categorization, Association Rules, Medical Images

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Alexandru Coman, Maria-Luiza Antonie, Osmar R. Zaïane
• Keywords: Association Rule Mining, Data Cleaning Phase, Data Mining, KDD 2002, WORDS Mammography Mining