Extracting spatial association rules from the maximum frequent itemsets based on Boolean matrix

Mining spatial association rules is one of the most important branches in the field of Spatial Data Mining (SDM). Because of the complexity of spatial data, a traditional method in extracting spatial association rules is to transform spatial database into general transaction database. The Apriori algorithm is one of the most commonly used methods in mining association rules at present. But a shortcoming of the algorithm is that its performance on the large database is inefficient. The present paper proposed a new algorithm by extracting maximum frequent itemsets based on a Boolean matrix. And a case study about extracting the spatial association rules between land cover and terrain factors was demonstrated to show the validation of the new algorithm. Finally, the conclusion was reached by the comparison between the Apriori algorithm and the new one which revealed that the new algorithm improves the efficiency of extracting spatial association rules.