Mining spatial co-location patterns with dynamic neighborhood constraint

Abstract

Spatial co-location pattern mining is an interesting and important issue in spatial data mining area which discovers the subsets of features whose events are frequently located together in geographic space. However, previous research literatures for mining co-location patterns assume a static neighborhood constraint that apparently introduces many drawbacks. In this paper, we conclude the preferences that algorithms rely on when making decisions for mining co-location patterns with dynamic neighborhood constraint. Based on this, we define the mining task as an optimization problem and propose a greedy algorithm for mining co-location patterns with dynamic neighborhood constraint. The experimental evaluation on a real world data set shows that our algorithm has a better capability than the previous approach on finding co-location patterns together with the consideration of the distribution of data set. © 2009 Springer Berlin Heidelberg.