Co-location pattern mining using approximate Euclidean measure

Abstract

Co-location discovery plays an important role in spatial data mining. It aims to find types of objects that are frequently located together in a spatial neighborhood. A very popular interestingness measure for co-locations requires knowledge of all objects participating in co-location instances. A common requirement for co-location instances in the literature is that all objects contained in them are pairwise neighbors. Typically, this is determined in quadratic time with respect to the number of objects. In this paper, we introduce a new framework for determining pairwise neighborhoods in linear time. The framework utilizes a new metric that generates approximately the same neighborhoods as the Euclidean metric. We provide modifications of two algorithms for co-location instance identification that employ the proposed approach. Experiments performed on two real-world datasets demonstrate that we can achieve better processing times than using the state-of-the-art approach.