Discovery of generalized spatial association rules

Abstract

Spatial association rule mining is an important technique of spatial data mining and business intelligence. Nevertheless, traditional spatial association rule mining approaches have a significant limitation that they cannot effectively involve and exploit non-spatial information. As a result, many interesting rules mixing spatial and non-spatial information which provide extra insights and tell the hidden patterns cannot be found. In this paper, we propose a novel approach to discover the Generalized Spatial Association Rules (GSAR), which are capable of expressing richer information including not only spatial, but also non-spatial and taxonomy information of spatial objects. Meanwhile, the additional computation introduced only costs linear time complexity. A case study on a real crime dataset shows that using the proposed approach, many interesting and meaningful crime patterns can be discovered. However, traditional approaches cannot find such patterns at all.