Filtering Frequent Spatial Patterns with Qualitative Spatial Reasoning

In frequent geographic pattern mining a large amount of patterns can be non-novel and non-interesting. This problem has been addressed recently, and background knowledge is used to reduce well known geographic patterns. However, a large amount of meaningless patterns which is independent of domain knowledge is still extracted from geographic data. Therefore, this paper proposes a method for filtering specific types of meaningless spatial patterns using qualitative spatial reasoning. We proof significant reduction of the number of frequent patterns, which is also shown with experiments performed on real data. These experiments even show a reduction in computational time.