A review on spectral clustering and stochastic block models

被引：7
|
作者：
Baek, Mina [1 ]
Kim, Choongrak [1 ]
机构：
[1] Pusan Natl Univ, Busan, South Korea
来源：
JOURNAL OF THE KOREAN STATISTICAL SOCIETY | 2021年 / 50卷 / 03期
基金：
新加坡国家研究基金会;
关键词：
Dimension reduction; Manifold; Random graph; Regularization; Spectral embedding; NUMBER; REGULARIZATION; SELECTION;
D O I：
10.1007/s42952-021-00112-w
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Clustering is an important statistical tool for the analysis of unsupervised data. Spectral clustering and stochastic block models, based on networks and graphs, are well established and widely used for community detection among many clustering algorithms. In this paper we review and discuss important statistical issues in spectral clustering and stochastic block models.
引用
收藏
页码：818 / 831
页数：14
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