Strategies of an Efficient Algorithm PARM to Generate Association Rules Mining Technique Based on Spatial Data

In the Association rule mining, originally proposed form market basket data, has potential applications in many areas. Spatial data, such as remote sensed imagery (RSI) data, is one of the promising application areas. Association Rule mining is one of the most popular data mining techniques which can be defined as extracting the interesting correlation and relation among large volume of transactions. Extracting interesting patterns and rules from spatial data sets, composed of images and associated ground data, can be of importance in precision agriculture, resource discovery, and other areas. However, in most cases, the sizes of the spatial data sets are too large to be mined in a reasonable amount of time using existing algorithms. In this paper, we propose an efficient approach to derive association rules from spatial data using Peano Count Tree (P-tree) structure. P-tree structure provides a lossless and compressed representation of spatial data. Based on P-trees, an efficient association rule mining algorithm with fast support calculation and significant pruning techniques is introduced to improve the efficiency of the rule mining process. KeywordsAssociation rule mining, Data mining, Remote sensed imagery (RSI), Spatial data.