Association Rule Mining Based on Image Content

Image mining is concerned with knowledge discovery in image databases. We present a data mining approach to find association rules based on image content. The Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and Association rule mining. The purpose of our experiments is to explore the feasibility of data mining approach.. Results will show that there is promise in image mining based on content. Mammography is one of the best methods in breast cancer detection, but in some cases, radiologists cannot detect tumors despite their experience. Computer-aided method using association rule could assist medical staff and improve the accuracy of detection. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset. Traditional association rule algorithms adopt an iterative method to discovery frequent item set, which requires very large calculations and a complicated transaction process. Because of this, a new association rule algorithm is proposed in this paper. Experimental results show that this new method can quickly discover frequent item sets and effectively mine potential association rules.