Spatial association rule discovery in spatial databases is a very important data mining task. In this paper

a two stage strategy for the discovery of spatial association rules in geographical databases is proposed. The spatial computational overhead is greatly reduced by top down refinement of spatial predicate granularities and multiple recursions of single level boolean association rule discovery step

which is the key step of the algorithm. The single level boolean association rule mining algorithm

FPT Generate

is detailed. FPT Generate uses the frequent item prefix tree

FIPT

to compress and project frequent item sets

and discovers association rules by growing a frequent pattern tree

FPT

by depth first search. The algorithm FPT Generate generates association rules without candidate generation and without redundant scans of databases. Optimizing techniques for the implementation

such as pseudo projecting and pruning

dynamic threading and hashing

and disk based partitioning

are also discussed. Experiments show that spatial association discovery systems powered by FPT Generate are much more time efficient and space scalable than those powered by the classical algorithm

Apriori. Finally

a spatial association rule discovery system

SmartMiner

upon the support of MapInfo Professional

is developed.