2026-07-21T23:47:43Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18898 2024-03-06T11:02:54Z ddc:004 open_access

Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results Ghosh, Subhankar Gupta, Jayant Sharma, Arun An, Shuai Shekhar, Shashi Colocation pattern Participation index Multiple comparisons problem Spatial heterogeneity Statistical significance Given a set 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 C of S> such that 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 [Subhankar et. al, 2022] 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Subhankar Ghosh and Jayant Gupta and Arun Sharma and Shuai An and Shashi Shekhar 2023 Is Part Of LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2023.3 urn:nbn:de:0030-drops-188985 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.3 eng https://creativecommons.org/licenses/by/4.0/legalcode