A New Approach to Generate Frequent Itemsets in Mining Image Association Rules

This paper proposes an approach to generate frequent itemsets in mining image association rules——Frequent Item Tree.We utilize bSQ image format to re-organize the image data to apply frequent item tree.Moreover, this paper propose several optimization techniques, including frequent item tree pruning, semi-depth-first search, image mask, and multi-level gray generation, to decrease the time and space complexity.