{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T02:06:29Z","timestamp":1769565989703,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686448","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,27]]},"abstract":"<jats:p>Spatial co-location patterns (SCPs) are defined as sets of spatial features whose instances are frequently located near each other. While fuzzy spatial structures have advanced SCP mining by modeling real-world spatial uncertainty, they often incur high computational costs and lead to explosive growth in redundant co-locations. Additionally, traditional fuzzy SCP representations lack precision in capturing semantically meaningful spatial relationships, limiting their usefulness for decision support. To address these challenges, this paper proposes a novel fuzzy \u03b2-covering-based mining framework, which: (1) employs fuzzy clustering for adaptive spatial soft partitioning to handle uncertainty; (2) introduces a Fuzzy Covering Participation Rate (FCPR) metric to rigorously quantify feature contributions. Comprehensive experiments on two real-world datasets and multiple synthetic benchmarks demonstrate that the proposed approach mitigates pattern explosion by 14.3\u201376.9%, substantially improving both computational efficiency and analytical utility in uncertainty-aware spatial pattern mining.<\/jats:p>","DOI":"10.3233\/faia251649","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:18:52Z","timestamp":1769519932000},"source":"Crossref","is-referenced-by-count":0,"title":["A New Compression Strategy for Spatial Co-Location Patterns Based on Fuzzy \u03b2 Covering"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1745-0971","authenticated-orcid":false,"given":"Chunhu","family":"Luo","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China"}]},{"given":"Yue","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0922-4719","authenticated-orcid":false,"given":"Xiaoxuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China"}]},{"given":"Pan","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China"}]},{"given":"Wen","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining XI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251649","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:18:52Z","timestamp":1769519932000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251649"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,27]]},"ISBN":["9781643686448"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251649","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,27]]}}}