New Spatial Multi-Dimensional Association Rule Model and Its Algorithm
YE Shui-sheng · Nanjing Hangkong Hangtian Daxue xuebao · 2005
Spatial association rule mining based on spatial objects is more difficult than relational association rule mining because the spatial object attributes are spatially auto-correlated, continuous and multidimensional. It is more difficult to define transactions in traditional association rule mining. This paper establishes a new spatial multi-dimensional association rules model (SMARM) for multi-dimensional spatial data mining, where spatial transactions are defined by a notion of impact zone based on spatial autocorrelation and replaced by a traditional transaction definition. A new mining algorithm (SMARBIA) is realized. The algorithm can avoid enormous candidate items in mining process by pruning techniques based on impact zone and spatial support. Finally the experiment shows that it can decrease the number of candidate items.