Algorithms for spatial colocation pattern mining

Abstract

Spatial data mining is a specialized field that focuses on extracting meaningful insights and patterns from geographical or spatial data. One particular area of interest in spatial data mining is colocation pattern mining. Colocation patterns refer to objects or entities that tend to occur frequently in close spatial proximity to each other. These patterns can provide valuable insights into spatial relationships and dependencies. Traditional colocation mining algorithms typically operate on static data and re-quire a predefined single-distance threshold to determine spatial proximity. However, deciding on a suitable threshold can be challenging and may not capture the full range of interesting patterns. Moreover, processing the graph representation of spatial data and handling dynamic or evolving datasets present additional challenges in colocation pattern mining. To address these challenges, our work introduces several novel approaches. Firstly, we propose a new colocation query called Range colocation mining. This query enables the computation of colocation patterns over a range of distances, rather than relying on a single threshold value. This provides greater flexibility to analysts when the de-termination of a specific distance threshold is difficult or uncertain. Unlike classical algorithms that compute patterns separately for each distance threshold, our method efficiently computes patterns in a single scan over the spatial data, ensuring scalability. In addition, we extend the traditional notion of colocation patterns beyond cliques to any subgraph representation. This notion allows for a broader exploration of patterns and considers the edges labels and the degree of affinity between objects. We analyze the complexity of mining subgraph colocation patterns and propose a novel query for high-utility subgraph (colocation) pattern mining. The problem turns out to be more complex than the classical colocation pattern mining.