| dc.contributor.author | Karabina, Armagan | |
| dc.contributor.author | Kilic, Erdal | |
| dc.date.accessioned | 2020-06-21T13:39:31Z | |
| dc.date.available | 2020-06-21T13:39:31Z | |
| dc.date.issued | 2016 | |
| dc.identifier.isbn | 978-1-5090-1679-2 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12712/13622 | |
| dc.description | 24th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEY | en_US |
| dc.description | WOS: 000391250900427 | en_US |
| dc.description.abstract | The most important one of main problems for K-based clustering algorithm is randomly selected k parameter when running the algorithm. In this study, an automated clustering algorithm based on agglomerative clustering and clusters without taking k parameter from user have been proposed. The main objective of the study is to select correct k value by using Spearman's Correlation Coefficient. This newly proposed automatic k parameter selection method's performance was examined in the study. | en_US |
| dc.description.sponsorship | IEEE, Bulent Ecevit Univ, Dept Elect & Elect Engn, Bulent Ecevit Univ, Dept Biomed Engn, Bulent Ecevit Univ, Dept Comp Engn | en_US |
| dc.language.iso | tur | en_US |
| dc.publisher | Ieee | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | agglomerative clustering | en_US |
| dc.subject | unsupervised learning | en_US |
| dc.subject | k clustering | en_US |
| dc.subject | correct k value selection | en_US |
| dc.title | An Automated Clustering Algorithm Based on Agglomerative Clustering | en_US |
| dc.type | conferenceObject | en_US |
| dc.contributor.department | OMÜ | en_US |
| dc.identifier.startpage | 1801 | en_US |
| dc.identifier.endpage | 1804 | en_US |
| dc.relation.journal | 2016 24Th Signal Processing and Communication Application Conference (Siu) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
