{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T05:03:23Z","timestamp":1667279003545},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683461","type":"print"},{"value":"9781643683478","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:00:00Z","timestamp":1666051200000},"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":[[2022,10,18]]},"abstract":"<jats:p>Spatial sub-frequent co-location patterns reveal the rich spatial relation-ship of spatial features and instances, which are widely used in real applications such as environmental protection, urban computing, public transportation, and so on. Existing sub-frequent pattern mining methods cannot distinguish patterns whose row instance spatial distributions are significantly different. Additionally, patterns whose row instances are tightly located in a local area can further reveal the particularity of the local area such as special environments and functions. Therefore, this paper proposes mining Local Tight Spatial Sub-frequent Co-location Patterns (LTSCPs). First, a relevancy index is presented to measure the local tightness between sub-frequent pattern row instances by analyzing mutual participation instances between row instances. The concept of LTSCPs is then proposed followed by an algorithm for mining these LTSCPs. Finally, a large number of experiments are carried out on synthetic and real datasets. The results show that the algorithm for mining LTSCPs is efficient and LTSCPs are practical.<\/jats:p>","DOI":"10.3233\/faia220365","type":"book-chapter","created":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T09:31:24Z","timestamp":1667208684000},"source":"Crossref","is-referenced-by-count":0,"title":["Mining Local Tight Spatial Sub-Prevalent Co-Location Patterns"],"prefix":"10.3233","author":[{"given":"Qiuqing","family":"He","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Yunnan, China"}]},{"given":"Hongmei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Yunnan, China"}]},{"given":"Qing","family":"Xiao","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 VIII"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220365","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T09:32:08Z","timestamp":1667208728000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220365"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,18]]},"ISBN":["9781643683461","9781643683478"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220365","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,18]]}}}