{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T23:26:04Z","timestamp":1648855564781},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"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":[[2021,10,14]]},"abstract":"<jats:p>Fuzzy Subtractive Clustering (FSC) is a technique of fuzzy clustering where the cluster to be formed is unknown. The distance function in the FSC method has an important role in determining the number of points that have the most neighbors. Therefore, this study uses several distance functions. The results obtained indicate that the DBI results indicate that the Euclidean distance has a good cluster evaluation result in the number of clusters 4. Meanwhile, for the PC value the combination of the Minkowski Chebysev distance produces a good PC value in the number of clusters 2.<\/jats:p>","DOI":"10.3233\/faia210204","type":"book-chapter","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:53:54Z","timestamp":1634766834000},"source":"Crossref","is-referenced-by-count":0,"title":["An Optimization of Several Distance Function on Fuzzy Subtractive Clustering"],"prefix":"10.3233","author":[{"given":"Sugiyarto","family":"Surono","sequence":"first","affiliation":[{"name":"Department of Mathematics, FAST Ahmad Dahlan University Yogyakarta, Indonesia"}]},{"given":"Annisa Eka","family":"Haryati","sequence":"additional","affiliation":[{"name":"Magister Math. Education Ahmad Dahlan University Yogyakarta, Indonesia"}]},{"given":"Joko","family":"Eliyanto","sequence":"additional","affiliation":[{"name":"Magister Math. Education Ahmad Dahlan University Yogyakarta, Indonesia"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining VII"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA210204","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T13:40:42Z","timestamp":1635169242000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA210204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia210204","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,14]]}}}