Mining top-k-size maximal co-location patterns

Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. It is difficult to discover co-location patterns because of the huge amount of space data. A common framework for mining spatial co-location patterns employs a level-wised search method to discover co-location patterns, and generates numerous redundant patterns which need huge cost of space storage and time consumption. Longer size patterns may have more interesting information for users, which causes the requirement for mining longer size patterns preferentially. In this paper, a novel algorithm is proposed to discover compact co-location patterns called top-k-size maximal co-location patterns by introducing a new data structure - MCP-tree, where k is a desired number of distinct sizes of mined co-location patterns. Our algorithm doesn't need to generate all candidate co-locations and it only checks partial candidates to mine top-k-size maximal co-location patterns, so it needs less space and costs less time. The experiment result shows that the proposed algorithm is efficient.