KNN-SC: novel spectral clustering algorithm using k-nearest neighbors
dc.contributor.author | Kim, Jeong-Hun | |
dc.contributor.author | Choi, Jong-Hyeok | |
dc.contributor.author | Park, Young-Ho | |
dc.contributor.author | Leung, Carson | |
dc.contributor.author | Nasridinov, Aziz | |
dc.date.accessioned | 2022-01-25T17:09:19Z | |
dc.date.available | 2022-01-25T17:09:19Z | |
dc.date.issued | 2021-11-15 | |
dc.date.submitted | 2022-01-22T04:17:36Z | en_US |
dc.description.abstract | Spectral 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.sponsorship | National 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, Canada | en_US |
dc.identifier.citation | J. 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.doi | 10.1109/ACCESS.2021.3126854 | |
dc.identifier.uri | http://hdl.handle.net/1993/36215 | |
dc.language.iso | eng | en_US |
dc.publisher | IEEE Access | en_US |
dc.rights | open access | en_US |
dc.subject | k-nearest neighbors | en_US |
dc.subject | nearest neighbor graph | en_US |
dc.subject | potential noise detection | en_US |
dc.subject | spectral clustering | en_US |
dc.title | KNN-SC: novel spectral clustering algorithm using k-nearest neighbors | en_US |
dc.type | Article | en_US |
local.author.affiliation | Faculty of Science | en_US |
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