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http://dx.doi.org/10.5351/KJAS.2020.33.2.115

Spectral clustering: summary and recent research issues  

Jeong, Sanghun (Department of Statistics, Pusan National University)
Bae, Suhyeon (Department of Statistics, Pusan National University)
Kim, Choongrak (Department of Statistics, Pusan National University)
Publication Information
The Korean Journal of Applied Statistics / v.33, no.2, 2020 , pp. 115-122 More about this Journal
Abstract
K-means clustering uses a spherical or elliptical metric to group data points; however, it does not work well for non-convex data such as the concentric circles. Spectral clustering, based on graph theory, is a generalized and robust technique to deal with non-standard type of data such as non-convex data. Results obtained by spectral clustering often outperform traditional clustering such as K-means. In this paper, we review spectral clustering and show important issues in spectral clustering such as determining the number of clusters K, estimation of scale parameter in the adjacency of two points, and the dimension reduction technique in clustering high-dimensional data.
Keywords
adjacency; dimension reduction; number of clusters; scale parameter;
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