Mining top-k closed co-location patterns

In this paper, we present a problem to discover compact co-location patterns without minimum prevalence threshold. A spatial co-location is a set of spatial events being frequently observed together in nearby geographic space. A common framework for mining spatial co-location patterns employs a level-wised search method (like Apriori) to discover co-location patterns, and generates numerous redundant patterns since all of the 2^l subsets of each length l event set the algorithms discover are included in the result set. In addition, most works of spatial co-location mining require the specification of a minimum prevalent threshold to find interesting co-location patterns. However, it is difficult for users to decide an appropriate threshold value without prior knowledge of their task-specific spatial data. To solve these problems, we propose a problem to mine top-k closed co-location patterns, where k is the desired number of patterns, and develop an algorithm to efficiently find the interesting patterns. The experiment result shows that the proposed algorithm is effective in computation.