Mammography Classification by an Association Rule-Based Classifier
- Osmar R. Zaiane, University of Alberta (Database)
- Maria-Luiza Antonie, Dept. of Computing Science UofA
- Alexandru Coman
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.
Citation
O. Zaiane, M. Antonie, A. Coman. "Mammography Classification by an Association Rule-Based Classifier". International ACM SIGKDD Workshop on Multimedia Data Mining, Springer Verlag, pp 62-69, July 2002.| Keywords: | Mammography Mining, Image Classification, Document Categorization, Association Rules, Medical Images |
| Category: | In Workshop |
| Web Links: | ACM Digital Library |
BibTeX
@misc{Zaiane+al:MDM/KDD02,
author = {Osmar R. Zaiane and Maria-Luiza Antonie and Alexandru Coman},
title = {Mammography Classification by an Association Rule-Based Classifier},
Publisher = {Springer Verlag},
Pages = {62-69},
booktitle = {International ACM SIGKDD Workshop on Multimedia Data Mining},
year = 2002,
}Last Updated: March 05, 2020Submitted by Sabina P
