An intelligent approach for mining frequent patterns in spatial database system using SQL

Mining frequent pattern from spatial databases systems has always remained a challenge for researchers. However, the performance of SQL based spatial data mining is known to fall behind specialized implementation since the prohibitive nature of the cost associated with extracting knowledge, and the lack of suitable declarative query language support. In this paper, we proposed an enhancement of existing mining algorithm based on SQL for the problem of finding frequent patterns for efficiently mining frequent patterns of spatial objects occurring in space. The proposed algorithm is termed as Frequent Positive Association Rule/Frequent Negative Association Rule (FPAR/FNAR). This algorithm is an improvement of the FP growth algorithm. Further an enhancement of the improved algorithm by a numerical method based on SQL for generating frequent patterns known as Transaction Frequent Pattern (TFP) Tree is proposed to reduces the storage space of the spatial dataset and overcomes some limitations of the previous method.