Optimal candidate generation in spatial co-location mining

Existing level-wise spatial co-location algorithms suffer from generating extra, non-clique candidate instances and thus requires cliqueness checking at every level. In this paper, we propose a novel, spatial co-location mining algorithm which automatically generates co-located spatial features without generating any non-clique candidates at any level. Subsequently our algorithm generates less candidates than other existing level-wise co-location algorithms without losing any information. The benefit of our algorithm has been clearly observed at an earlier stage in the mining process.