Mining Regional High Utility Co-location Pattern

A co-location pattern is a set of spatial features whose instances are frequently located together in geo-space. In real world, different instances have different distributions and different values. However, existing methods for mining pattern ignore these differences. In this paper, we propose a novel method for mining regional high utility co-location pattern by considering both instance distribution and value. First, local regions are obtained based on fuzzy density peak clustering. Then, the regional high utility co-location pattern is defined, and an efficient algorithm for mining the patterns in local regions is presented by pruning unpromising patterns. The experiment results show the patterns are meaningful and the mining algorithm is efficient.