Discovering worthy spatial co-location patterns based on pattern distributions through clustering

Abstract

Spatial co-location pattern mining aims to uncover associations among spatial features, enabling users to discover correlation knowledge from spatial datasets. However, as spatial datasets grow, traditional frameworks for mining co-location patterns produce an overwhelming number of redundant results, which complicates further analysis. This paper focuses on extracting worthy co-location patterns, which are concise summaries of prevalent co-location patterns. We introduce two similarity measures—feature-based similarity and distribution-based similarity—to evaluate redundancy between co-location patterns from both feature and instance perspectives. Using these measures, we propose a novel approach called the Worthy Co-location Patterns Mining algorithm (WCPM) to condense prevalent co-location patterns. Initially, we employ a clique-based method to discover prevalent co-location patterns and categorize them into Maximal Co-location Patterns (MCPs) and Non-Maximal Co-location Patterns (NMCPs). Subsequently, we cluster the MCPs to extract the feature-similar MCPs, and based on distribution similarity, identify the worthy MCPs from the clustering results. Finally, we design a top-down algorithm to mine Worthy Non-Maximal Co-location Patterns (WNMCPs). Experiments on both synthetic and real datasets demonstrate that WCPM outperforms similar state-of-the-art approaches in terms of compression power and running time.