Association Rule Mining on Remotely Sensed Images Using P-trees

Association Rule Mining, originally proposed for market basket data, has potential applications in many areas. Remote Sensed Imagery (RSI) data is one of the promising application areas. Extracting interesting patterns and rules from datasets composed of images and associated ground data, can be of importance in precision agriculture, community planning, resource discovery and other areas. However, in most cases the image data sizes are too large to be mined in a reasonable amount of time using existing algorithms. In this paper, we propose an approach to derive association rules on RSI data using Peano Count Tree (P-tree) structure. P-tree structure, proposed in our previous work [21], provides a lossless and compressed representation of image data. Based on P-trees, an efficient association rule mining algorithm P-ARM with fast support calculation and significant pruning techniques are introduced to improve the efficiency of the rule mining process. P-ARM algorithm is implemented and compared with FP-growth and Apriori algorithms. Experimental results showed that our algorithm is superior for association rule mining on RSI spatial data.