Spatial Constraint Topology Association Rules Mining Based on Apriori

This paper proposes an algorithm of mining spatial topology association rules with constraint condition based on Apriori, which is used to mining spatial multilayer transverse association rules with constraint condition from large spatial database. This algorithm generates candidate frequent topological item sets via up search strategy similar to Apriori, which is suitable for mining short spatial topological frequent item sets with constraint condition. This algorithm compresses storage structure of spatial topological relation to create an integer. Via this method, firstly, the algorithm may efficiently reduce some storage space of mining database. Secondly, the algorithm is effortless to distinguish topological relation of two spatial objects, namely, it may fast compute support of candidate item sets. Finally, the algorithm is fast to connect (k+1)-candidate item sets of k-frequent item set by up search strategy. The result of experiment indicates that the algorithm of mining spatial topology association rules with constraint condition based on Apriori is able to extract spatial multilayer transverse association rules with constraint condition from spatial database via efficient data store, and it is very efficient to extract short frequent topology association rules with constraint condition.