{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T04:50:33Z","timestamp":1762059033020,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T00:00:00Z","timestamp":1658534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China OF FUNDER","award":["61966036","62066023","2018HC019","XJZZ1706","202205AF150031"],"award-info":[{"award-number":["61966036","62066023","2018HC019","XJZZ1706","202205AF150031"]}]},{"name":"Innovative Research Team of Yunnan Province OF FUNDER","award":["61966036","62066023","2018HC019","XJZZ1706","202205AF150031"],"award-info":[{"award-number":["61966036","62066023","2018HC019","XJZZ1706","202205AF150031"]}]},{"name":"Kunming University OF FUNDER","award":["61966036","62066023","2018HC019","XJZZ1706","202205AF150031"],"award-info":[{"award-number":["61966036","62066023","2018HC019","XJZZ1706","202205AF150031"]}]},{"name":"Li Zhengqiang Expert Workstation of Yunnan Province OF FUNDER","award":["61966036","62066023","2018HC019","XJZZ1706","202205AF150031"],"award-info":[{"award-number":["61966036","62066023","2018HC019","XJZZ1706","202205AF150031"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>A co-location pattern is a set of spatial features whose instances are frequently correlated to each other in space. Its mining models always consist of two essential steps. One step is to generate neighbor relationships between spatial instances, and another step is to check the prevalence of candidate patterns on the clique, star or Delaunay triangulation relationships. At least three major issues are addressed in this paper. First, since different spatial regions, different distribution densities, it is difficult to set appropriate parameters to generate ideal neighbor relationships. Second, the clique relationship and the others are so strongly rigid that the users\u2019 personal interests are suppressed; some interesting patterns are neglected without increasing redundancy. Third, the different strength of correlations among instances are neglected in prevalence calculation. It causes correlations among features to be undifferentiated. Accordingly, the main work of this paper includes: (1) The neighbor relationship generation can be improved on the idea that the distances between an instance and any of its neighbors are not remarkably different. (2) The type-\u03b2 co-location pattern is defined and checked based on a co-occurrence where the closeness centrality of each instance is not less than a given threshold \u03b2. (3) Since the closeness centrality carries strength of correlations among instances, the strength of the correlations between a feature and the other ones in a type-\u03b2 co-location pattern can be evaluated with prevalence calculation. Finally, experiments on synthetic and real-world spatial data sets are used to assess the effectiveness and efficiency of our works. The results show that fewer spatial neighbor relationships are generated, and more interesting patterns can be discovered by flexibly adjusting \u03b2 according to the user\u2019s preferences.<\/jats:p>","DOI":"10.3390\/ijgi11080418","type":"journal-article","created":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T22:49:02Z","timestamp":1658702942000},"page":"418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Mining Type-\u03b2 Co-Location Patterns on Closeness Centrality in Spatial Data Sets"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7301-6148","authenticated-orcid":false,"given":"Muquan","family":"Zou","sequence":"first","affiliation":[{"name":"Department of Computer and Engineering, Yunnan University, Kunming 650091, China"},{"name":"School of Information Engineering, Kunming University, Kunming 650214, China"},{"name":"Key Laboratory of Data Governance and Intelligent Decision in Universities of Yunnan, Kunming University, Kunming 650214, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2214-2299","authenticated-orcid":false,"given":"Lizhen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer and Engineering, Yunnan University, Kunming 650091, China"},{"name":"Department of Computer Science and Engineering, Dianchi College, Yunnan University, Kunming 650228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingping","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computer and Engineering, Yunnan University, Kunming 650091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vanha","family":"Tran","sequence":"additional","affiliation":[{"name":"Departement of Information Technology Specialization, FPT University, Hoa Lac High Tech Park, Hanoi 155514, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1109\/TFUZZ.2021.3074074","article-title":"Spatial co-location pattern discovery Incorporating Fuzzy Theory","volume":"30","author":"Wang","year":"2021","journal-title":"IEEE Trans. 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