Mining Association Patterns From Neighborhood Insight

Detecting and identifying complex association patterns between two variables is a fundamental task. This requires association measures that satisfy both generality (the ability to capture a wide range of association structures) and equitability (the absence of bias toward specific association types). Designing such measures is challenging due to the distributional uncertainty, structural diversity, and mixture of association types found in large datasets. Granular computing offers a promising direction, as local neighborhood structures naturally encode multi-scale association information. Inspired by this insight, we introduce the maximal neighborhood coefficient (MNC), an association measure based on $k$k-NN granulation. MNC captures a broad range of associations without empirical bias while retaining local structural details often missed by existing measures. Extending this idea, we develop a family of maximal neighborhood nonparametric exploration (MNNE) statistics that supply richer auxiliary information for characterizing associations. Together, MNC and MNNE form a data-driven exploration toolkit that offers strong empirical performance and a new perspective on mining complex association patterns.