Existing spatial co-location algorithms based on levels suffer from generating extra, nonclique candidate instances. Thus, they require cliqueness checking at every level. In this thesis, a novel, spatial co-location mining algorithm that automatically generates co-located spatial features without generating any nonclique candidates at any level is proposed. Subsequently, this algorithm generates fewer candidates than other existing level-wise, co-location algorithms without losing any pertinent information. The benefits of this algorithm have been clearly observed at early stages in the mining process.(77 pages) iv ACKNOWLEDGMENTS

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 each level. Subsequently our algorithm is more efficient than other existing level-wise co-location algorithms because no cliqueness checking is performed in our algorithm. In addition, our algorithm produces a smaller number of co-location candidates than the other existing algorithms.