PARM-An Efficient Algorithm to Mine Association Rules From Spatial Data

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 38; no. 6; pp. 1513 - 1524
• Main Authors: Qin Ding, Qiang Ding, Perrizo, W.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2008
• Subjects: Agriculture; Algorithms; Artificial Intelligence; Association; Association rule mining; Association rules; Computer science; Computer Simulation; Crops; Data mining; Databases, Factual; Information Storage and Retrieval - methods; Logistic Models; Neodymium; Pattern Recognition, Automated - methods; remote sensed imagery (RSI); Remote sensing; Satellites; spatial data; Statistics as Topic; Telecommunications; Telescopes; spatial data; Association rule mining; remote sensed imagery (RSI); data mining
• ISSN: 1083-4419; 1941-0492; 1941-0492
• DOI: 10.1109/TSMCB.2008.927730

Association rule mining, originally proposed for 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. 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 PARM with fast support calculation and significant pruning techniques is introduced to improve the efficiency of the rule mining process. The P-tree based association rule mining (PARM) 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.