{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:41:59Z","timestamp":1777696919714,"version":"3.51.4"},"reference-count":39,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U22A2099"],"award-info":[{"award-number":["No. U22A2099"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61966009"],"award-info":[{"award-number":["No. 61966009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62006057"],"award-info":[{"award-number":["No. 62006057"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Natural Science Foundation of Guangxi Province","doi-asserted-by":"publisher","award":["No. 2025GXNSFAA069551"],"award-info":[{"award-number":["No. 2025GXNSFAA069551"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Data Analysis: An International Journal"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:p>Spatial co-location pattern mining aims to uncover associations among spatial features, enabling users to discover correlation knowledge from spatial datasets. However, as spatial datasets grow, traditional frameworks for mining co-location patterns produce an overwhelming number of redundant results, which complicates further analysis. This paper focuses on extracting worthy co-location patterns, which are concise summaries of prevalent co-location patterns. We introduce two similarity measures\u2014feature-based similarity and distribution-based similarity\u2014to evaluate redundancy between co-location patterns from both feature and instance perspectives. Using these measures, we propose a novel approach called the Worthy Co-location Patterns Mining algorithm (WCPM) to condense prevalent co-location patterns. Initially, we employ a clique-based method to discover prevalent co-location patterns and categorize them into Maximal Co-location Patterns (MCPs) and Non-Maximal Co-location Patterns (NMCPs). Subsequently, we cluster the MCPs to extract the feature-similar MCPs, and based on distribution similarity, identify the worthy MCPs from the clustering results. Finally, we design a top-down algorithm to mine Worthy Non-Maximal Co-location Patterns (WNMCPs). Experiments on both synthetic and real datasets demonstrate that WCPM outperforms similar state-of-the-art approaches in terms of compression power and running time.<\/jats:p>","DOI":"10.1177\/1088467x251346483","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T05:25:12Z","timestamp":1748928312000},"page":"317-338","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Discovering worthy spatial co-location patterns based on pattern distributions through clustering"],"prefix":"10.1177","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9950-0053","authenticated-orcid":false,"given":"Xuguang","family":"Bao","sequence":"first","affiliation":[{"name":"Guilin University of Electronic Technology, Guilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3084-9008","authenticated-orcid":false,"given":"Yongming","family":"Huang","sequence":"additional","affiliation":[{"name":"Guilin University of Electronic Technology, Guilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8151-9451","authenticated-orcid":false,"given":"Shuaikang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Guilin University of Electronic Technology, Guilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7262-4707","authenticated-orcid":false,"given":"Liang","family":"Chang","sequence":"additional","affiliation":[{"name":"Guilin University of Electronic Technology, Guilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2004.90"},{"key":"e_1_3_3_3_2","doi-asserted-by":"crossref","unstructured":"Shekhar S Huang Y. Discovering spatial co-location patterns: a summary of results. In: International symposium on spatial and temporal databases 2001 pp.236\u2013256. Springer.","DOI":"10.1007\/3-540-47724-1_13"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10109-015-0216-4"},{"key":"e_1_3_3_5_2","doi-asserted-by":"crossref","unstructured":"Shu J Wang L Yang P et\u00a0al. Mining the potential relationships between cancer cases and industrial pollution based on high-influence ordered-pair patterns. In: International conference on advanced data mining and applications 2022 pp.27\u201340. Springer.","DOI":"10.1007\/978-3-031-22064-7_3"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2022.2029454"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2021.1981335"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.06.070"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3304365"},{"key":"e_1_3_3_10_2","doi-asserted-by":"crossref","unstructured":"Yoo JS Bow M. Mining maximal co-located event sets. In: Pacific-Asia conference on knowledge discovery and data mining 2011 pp.351\u2013362. Springer.","DOI":"10.1007\/978-3-642-20841-6_29"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.05.023"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.07.007"},{"key":"e_1_3_3_13_2","doi-asserted-by":"crossref","unstructured":"Yoo JS Bow M. Mining top-k closed co-location patterns. In: Proceedings 2011 IEEE international conference on spatial data mining and geographical knowledge services 2011 pp.100\u2013105. IEEE.","DOI":"10.1109\/ICSDM.2011.5969013"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.01.011"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3082628"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2759110"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-024-05296-2"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-232918"},{"key":"e_1_3_3_19_2","doi-asserted-by":"crossref","unstructured":"Yoo JS Shekhar S Smith J et\u00a0al. A partial join approach for mining co-location patterns. In: Proceedings of the 12th annual ACM international workshop on Geographic information systems 2004 pp.241\u2013249.","DOI":"10.1145\/1032222.1032258"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2006.150"},{"key":"e_1_3_3_21_2","doi-asserted-by":"crossref","unstructured":"Wang L Bao Y Lu J et\u00a0al. A new join-less approach for co-location pattern mining. In: 2008 8th IEEE international conference on computer and information technology 2008 pp.197\u2013202. IEEE.","DOI":"10.1109\/CIT.2008.4594673"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.03.072"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-173752"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-018-0646-2"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-216515"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119255"},{"key":"e_1_3_3_27_2","doi-asserted-by":"crossref","unstructured":"Wang L Bao X Cao L. Interactive probabilistic post-mining of user-preferred spatial co-location patterns. In: 2018 IEEE 34th international conference on data engineering (ICDE) 2008 pp.1256\u20131259. IEEE.","DOI":"10.1109\/ICDE.2018.00124"},{"key":"e_1_3_3_28_2","doi-asserted-by":"crossref","unstructured":"Bao X Wang L. Discovering interesting co-location patterns interactively using ontologies. In: Database Systems for Advanced Applications: DASFAA 2017 International Workshops: BDMS BDQM SeCoP and DMMOOC Suzhou China March 27-30 2017 Proceedings 22 2017 pp.75\u201389. Springer.","DOI":"10.1007\/978-3-319-55705-2_6"},{"key":"e_1_3_3_29_2","doi-asserted-by":"crossref","unstructured":"Bao X Wang L Chen H. Ontology-based interactive post-mining of interesting co-location patterns. In: Web technologies and applications: 18th Asia-pacific web conference APWeb 2016 Suzhou China September 23-25 2016. Proceedings Part II 2016 pp.406\u2013409. Springer.","DOI":"10.1007\/978-3-319-45817-5_35"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3054923"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-024-02155-x"},{"key":"e_1_3_3_32_2","unstructured":"Xin D Han J Yan X et\u00a0al. Mining compressed frequent-pattern sets. In: Proceedings of the 31st international conference on Very large data bases 2005 pp.709\u2013720."},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2020.2966182"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120407"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.01.059"},{"key":"e_1_3_3_36_2","doi-asserted-by":"crossref","unstructured":"Ma Y Lu J Yang D. Mining evolving spatial co-location patterns from spatio-temporal databases. In: 2022 IEEE International conference on big data and smart computing (BigComp) 2022 pp.129\u2013136. IEEE.","DOI":"10.1109\/BigComp54360.2022.00034"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110167"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dam.2024.04.022"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1080\/07468342.2018.1526020"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.12.007"}],"container-title":["Intelligent Data Analysis: An International Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1088467X251346483","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/1088467X251346483","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1088467X251346483","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:21:19Z","timestamp":1777454479000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/1088467X251346483"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,3]]},"references-count":39,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["10.1177\/1088467X251346483"],"URL":"https:\/\/doi.org\/10.1177\/1088467x251346483","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,3]]}}}