Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results

TL;DR

This paper introduces MultComp-RCM, an algorithm that reduces false discoveries and computational costs in regional colocation pattern mining by applying Bonferroni correction, improving reliability and efficiency.

Contribution

It presents a novel algorithm that effectively controls false discovery rates in regional colocation mining using Bonferroni correction, with proven theoretical and empirical benefits.

Findings

Reduces false discovery rate in colocation mining.

Decreases computational cost compared to previous methods.

Validated through experiments and case studies.

Abstract

Given a set \emph{S} of spatial feature types, its feature instances, a study area, and a neighbor relationship, the goal is to find pairs $<$a region ($r_{g}$), a subset \emph{C} of \emph{S}$>$ such that \emph{C} is a statistically significant regional-colocation pattern in $r_{g}$. This problem is important for applications in various domains including ecology, economics, and sociology. The problem is computationally challenging due to the exponential number of regional colocation patterns and candidate regions. Previously, we proposed a miner \cite{10.1145/3557989.3566158} that finds statistically significant regional colocation patterns. However, the numerous simultaneous statistical inferences raise the risk of false discoveries (also known as the multiple comparisons problem) and carry a high computational cost. We propose a novel algorithm, namely, multiple comparisons regional colocation miner (MultComp-RCM) which uses a Bonferroni correction. Theoretical analysis, experimental evaluation, and case study results show that the proposed method reduces both the false discovery rate and computational cost.