{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T15:29:37Z","timestamp":1648654177212},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"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":[[2020,11,9]]},"abstract":"<jats:p>There is a variety of interesting knowledge in spatial data sets. Spatial co-location pattern mining can discover sets of different features that are co-located. However, this type of pattern only lists the features that appear together without any consideration of the quantity ratio, which can cause confusion. For example, the co-location pattern church, restaurants shows that churches and restaurants are often close to each other, but information such as how many restaurants are near a church is usually not displayed. Also, in real spatial data sets, there is a mutual influence between spatial features, that is, a coupling relationship between different features or the same features. Thus, this paper proposes a novel spatial pattern called a coupling co-location pattern. First, we discuss the properties of the coupling phenomenon between spatial features, and then the concept of coupling co-location patterns is defined formally. Second, the measurement of support and mining framework for coupling co-location patterns are proposed. Finally, we conduct experiments on both real and synthetic data sets, and the results verify the practical significance of coupling co-location patterns.<\/jats:p>","DOI":"10.3233\/faia200708","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T17:31:39Z","timestamp":1605029499000},"source":"Crossref","is-referenced-by-count":0,"title":["The Coupling Co-Location Pattern: A New Spatial Pattern for Spatial Data Sets"],"prefix":"10.3233","author":[{"given":"Shiran","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Yunnan, China"}]},{"given":"Lizhen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Yunnan, China"}]},{"given":"Pingping","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Yunnan, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining VI"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200708","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T17:31:39Z","timestamp":1605029499000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200708"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200708","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,9]]}}}