Mining confident co-location rules without a support threshold
Yan Qun Huang, Hui Yun Xiong, Shashi Shekhar, Jian Pei · 2003
ABSTRACT Mining co-location patterns from spatial databases may reveal types of spatial features likely located as neighbors in space. In this paper, we address the problem of mining confident co-location rules without a support threshold. First, we propose a novel measure called the maximal participation index. We show that every confident co-location rule corresponds to a co-location pattern with a high maximal participation index value. Second, we show that the maximal participation index is non-monotonic, and thus the conventional Apriori-like pruning does not work directly. We identify an interesting weak monotonic property for the index and develop efficient algorithms to mine confident colocation rules. An extensive performance study shows that our method is both effective and efficient for large spatial databases. Keywords spatial data mining, confident co-location rules 1.