Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results
This addresses the multiple comparisons problem in spatial data mining for applications in ecology, economics, and sociology, but it is incremental as it builds on prior work with a specific correction method.
The paper tackled the problem of false discoveries and high computational cost in statistically significant regional-colocation mining by proposing a novel algorithm using Bonferroni correction, resulting in reduced false discovery rate and computational cost as shown in theoretical and experimental evaluations.
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.