Towards for Using Spectral Clustering in Graph Mining

被引：2

作者：

Ait El Mouden, Z. ^{[1
]}

Moulay Taj, R. ^{[2
]}

Jakimi, A. ^{[1
]}

Hajar, M. ^{[2
]}

机构：

[1] UMI, FSTE, Software Engn & Informat Syst Engn Team, Errachidia, Morocco

[2] UMI, FSTE, Operat Res & Comp Sci Team, Errachidia, Morocco

来源：

BIG DATA, CLOUD AND APPLICATIONS, BDCA 2018 | 2018年 / 872卷

关键词：

Community detection; Spectral clustering; Laplacian matrices; Similarity graphs;

D O I：

10.1007/978-3-319-96292-4_12

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an approach of community detection from data modeled by graphs, using the Spectral Clustering (SC) algorithms, and based on a matrix representation of the graphs. We will focus on the use of Laplacian matrices afterwards. The spectral analysis of those matrices can give us interesting details about the processed graph. The input of the process is a set of data and the output will be a set of communities or clusters that regroup the input data, by starting with the graphical modeling of the data and going through the matrix representation of the similarity graph, then the spectral analysis of the Laplacian matrices, the process will finish with the results interpretation.

引用

页码：144 / 159

页数：16

共 50 条

[31] Spectral Clustering Based on k-Nearest Neighbor Graph
Lucinska, Malgorzata
Wierzchon, Lawomir T.
COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT (CISIM), 2012, 7564 : 254 - 265
[32] Automatic graph pruning based on kernel alignment for spectral clustering
Alvarez-Meza, A. M.
Castro-Ospina, A. E.
Castellanos-Dominguez, G.
PATTERN RECOGNITION LETTERS, 2016, 70 : 8 - 16
[33] Multi-view Spectral Clustering Based on Graph Learning
Song, Jinmei
Liu, Baokai
Zhang, Kaiwu
Yu, Yao
Du, Shiqiang
2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 6527 - 6532
[34] EVOLUTIONARY SPECTRAL GRAPH CLUSTERING THROUGH SUBSPACE DISTANCE MEASURE
Al-Sharoa, Esraa
Aviyente, Selin
2016 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP), 2016,
[35] Fast Spectral Clustering via Efficient Multilayer Anchor Graph
Wei, Yiwei
Niu, Chao
Liu, Dejun
Ren, Peinan
INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING, 2024, 2024
[36] Application of Spectral Clustering Methods in Pipeline Systems Graph Models
Galimova, Gulnaz, I
Galimyanov, Il'nur D.
Yakupov, Dinar T.
Mokshin, Vladimir V.
HELIX, 2019, 9 (05): : 5607 - 5614
[37] Clustering using graph convolution networks
Al Jreidy, Maria
Constantin, Joseph
Dornaika, Fadi
Hamad, Denis
PROGRESS IN ARTIFICIAL INTELLIGENCE, 2024,
[38] A Further Study on the Degree-Corrected Spectral Clustering under Spectral Graph Theory
Liu, Fangmeng
Li, Wei
Zhong, Yiwen
SYMMETRY-BASEL, 2022, 14 (11):
[39] SPEAKER CLUSTERING USING VECTOR QUANTIZATION AND SPECTRAL CLUSTERING
Iso, Ken-ichi
2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2010, : 4986 - 4989
[40] Certifying Global Optimality of Graph Cuts via Semidefinite Relaxation: A Performance Guarantee for Spectral Clustering
Ling, Shuyang
Strohmer, Thomas
FOUNDATIONS OF COMPUTATIONAL MATHEMATICS, 2020, 20 (03) : 367 - 421

← 1 2 3 4 5 →