Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이민수 | * |
dc.date.accessioned | 2016-08-28T11:08:00Z | - |
dc.date.available | 2016-08-28T11:08:00Z | - |
dc.date.issued | 2012 | * |
dc.identifier.isbn | 9781450311564 | * |
dc.identifier.other | OAK-13892 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/229835 | - |
dc.description.abstract | An outlier is an object that is considerably dissimilar with the remainder of the dataset. In this paper, we first propose the notion of centrality and center-proximity as novel outlierness measures which can be considered to represent the characteristics of all of the objects in the dataset. We then propose a graph-based outlier detection method which can solve the problems of local density, micro-cluster, and fringe objects. Finally, through extensive experiments, we show the effectiveness of the proposed method. © 2012 ACM. | * |
dc.description.sponsorship | Special Interest Group on Information Retrieval (ACM SIGIR);ACM SIGWEB | * |
dc.language | English | * |
dc.title | Outlier detection using centrality and center-proximity | * |
dc.type | Conference Paper | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 2251 | * |
dc.relation.lastpage | 2254 | * |
dc.relation.journaltitle | ACM International Conference Proceeding Series | * |
dc.identifier.doi | 10.1145/2396761.2398613 | * |
dc.identifier.scopusid | 2-s2.0-84871089735 | * |
dc.author.google | Bae D.-H. | * |
dc.author.google | Jeong S. | * |
dc.author.google | Kim S.-W. | * |
dc.author.google | Lee M. | * |
dc.contributor.scopusid | 이민수(57195508191) | * |
dc.date.modifydate | 20240322133406 | * |