Show simple item record

dc.contributor.authorKim, Jeong-Hun
dc.contributor.authorChoi, Jong-Hyeok
dc.contributor.authorPark, Young-Ho
dc.contributor.authorLeung, Carson
dc.contributor.authorNasridinov, Aziz
dc.date.accessioned2022-01-25T17:09:19Z
dc.date.available2022-01-25T17:09:19Z
dc.date.issued2021-11-15
dc.date.submitted2022-01-22T04:17:36Zen_US
dc.identifier.citationJ. Kim, J. Choi, Y. Park, C.K. Leung, and A. Nasridinov, "KNN-SC: novel spectral clustering algorithm using k-nearest neighbors," IEEE Access, 2021; 9: 152616-152627.en_US
dc.identifier.urihttp://hdl.handle.net/1993/36215
dc.description.abstractSpectral clustering is a well-known graph-theoretic clustering algorithm. Although spectral clustering has several desirable advantages (such as the capability of discovering non-convex clusters and applicability to any data type), it often leads to incorrect clustering results because of high sensitivity to noise points. In this study, we propose a robust spectral clustering algorithm known as KNN-SC that can discover exact clusters by decreasing the influence of noise points. To achieve this goal, we present a novel approach that filters out potential noise points by estimating the density difference between data points using k -nearest neighbors. In addition, we introduce a novel method for generating a similarity graph in which various densities of data points are effectively represented by expanding the nearest neighbor graph. Experimental results on synthetic and real-world datasets demonstrate that KNN-SC achieves significant performance improvement over many state-of-the-art spectral clustering algorithms.en_US
dc.description.sponsorshipNational Research Foundation (NRF), South Korea; Institute for Information & Communication Technology Promotion (IITP), South Korea; Natural Sciences and Engineering Research Council of Canada (NSERC); University of Manitoba, Canadaen_US
dc.language.isoengen_US
dc.publisherIEEE Accessen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectk-nearest neighborsen_US
dc.subjectnearest neighbor graphen_US
dc.subjectpotential noise detectionen_US
dc.subjectspectral clusteringen_US
dc.titleKNN-SC: novel spectral clustering algorithm using k-nearest neighborsen_US
dc.typeArticleen_US
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1109/ACCESS.2021.3126854
local.author.affiliationFaculty of Scienceen_US


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record