Mining Co-location Patterns in Incremental Spatial Databases

An incremental database can be widely used in many areas due to the changes in data over time, including the location-based services (LBS), environmental ecology, and also the business behavior patterns. Looking for the spatial co-location pattern that appears frequently nearby over an incremental database has become an interesting and essential topic. Many spatial co-location pattern mining approaches are for traditional spatial databases. Therefore, they do not need to consider candidate instances generated and update their participation index in the process. Yoo et al. have proposed the EUCOLOC algorithm to mine co-location patterns in the incremental database. The EUCOLOC algorithm not only needs large storage to store points in the database and their relationships with each other, but also generates many unnecessary candidate instances. In this paper, we propose an approach to mine the co-location patterns for incremental database. Our approach can avoid generating non-incremental candidate instances and non-clique instances. Moreover, we also avoid storing the duplicated data. From our experimental results, we show that our method performed more efficiently than the EUCOLOC algorithm.