Redundancy Reduction for Prevalent Co-Location Patterns

Abstract

Spatial co-location pattern mining is an interesting and important task in spatial data mining which discovers the subsets of spatial features frequently observed together in nearby geographic space. However, the traditional framework of mining prevalent co-location patterns produces numerous redundant co-location patterns, which makes it hard for users to understand or apply. In this paper we study the problem of reducing redundancy in a collection of prevalent co-location patterns. We first introduce the concept of semantic distance between two co-location patterns, and then define redundant co-locations by introducing the concept of δ-covered, where δ (0≤δ≤1) is a coverage measure. We develop two algorithms RRclosed and RRnull to perform the redundancy reduction for prevalent co-location patterns. Our performance studies on the synthetic and real-world data sets demonstrate that our method effectively reduces the size of the original collection of closed co-location patterns by about 50%. Furthermore, the RRnull method runs much faster than the related closed co-location pattern mining algorithm.