Discovering Super-Colocation Patterns: A Summary of Results

Shuai An, Shesha Sadu, Arun Sharma, Majid Farhadloo, Shashi Shekhar

Abstract: Given a collection of Boolean spatial features, \emph{Super-Colocation Pattern Discovery} identifies subsets of features that are not only frequently located together but also have dense interactions. For example, the presence of multiple immune cells around cancer cells is more interesting to oncologists than a simple colocation between immune and cancer cells. This problem is important due to its societal applications, including oncology, transportation, and economic analysis. The problem is challenging due to the need to model interaction density among a subset of Boolean spatial features. Related work on colocation pattern mining is limited due to a lack of conceptual, logical, and physical models that accurately represent interaction density. Traditional interest measures (e.g., participation index) largely focus on the mere presence of another spatial feature type and overlook the number or density of neighboring instances. We propose a novel interest measure, termed Super-Colocation Density, which utilizes a matrix or tensor along with a utility-based index to quantify the interaction density among subsets of spatial features. We also introduce novel Super-Colocation Mining algorithms and evaluate the proposed methods through both theoretical analysis and experiments with real and synthetic data.