Discovering association rules based on image content

Our focus for data mining in the paper is concerned with knowledge discovery in image databases. We present a data mining algorithm to find association rules in 2-dimensional color images. The algorithm has four major steps: feature extraction, object identification, auxiliary image creation and object mining. Our emphasis is on data mining of image content without the use of auxiliary domain knowledge. The purpose of our experiments is to explore the feasibility of this approach. A synthetic image set containing geometric shapes was generated to test our initial algorithm implementation. Our experimental results show that there is promise in image mining based on content. We compare these results against the rules obtained from manually identifying the shapes. We analyze the reasons for discrepancies. We also suggest directions for future work.