JLMC: A clustering method based on Jordan-Form of Laplacian-Matrix

Niu,J, Fan,J and Stojmenovic,I 2014, JLMC: A clustering method based on Jordan-Form of Laplacian-Matrix, in IPCCC 2014 : Proceedings of the IEEE 33rd International Performance Computing and Communications Conference, IEEE, Piscataway, N.J., pp. 1-8, doi: 10.1109/PCCC.2014.7017060.

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Title JLMC: A clustering method based on Jordan-Form of Laplacian-Matrix
Author(s) Niu,J
Fan,J
Stojmenovic,I
Conference name Performance Computing and Communications. Conference (33rd : 2014 : Austin, Texas)
Conference location Austin, Texas
Conference dates 5-7 Dec. 2014
Title of proceedings IPCCC 2014 : Proceedings of the IEEE 33rd International Performance Computing and Communications Conference
Editor(s) [Unknown]
Publication date 2014
Conference series Performance Computing and Communications Conference
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) clustering algorithm
eigenvalue
Jordan-Form
Laplacian-Matrix
Summary Among the current clustering algorithms of complex networks, Laplacian-based spectral clustering algorithms have the advantage of rigorous mathematical basis and high accuracy. However, their applications are limited due to their dependence on prior knowledge, such as the number of clusters. For most of application scenarios, it is hard to obtain the number of clusters beforehand. To address this problem, we propose a novel clustering algorithm - Jordan-Form of Laplacian-Matrix based Clustering algorithm (JLMC). In JLMC, we propose a model to calculate the number (n) of clusters in a complex network based on the Jordan-Form of its corresponding Laplacian matrix. JLMC clusters the network into n clusters by using our proposed modularity density function (P function). We conduct extensive experiments over real and synthetic data, and the experimental results reveal that JLMC can accurately obtain the number of clusters in a complex network, and outperforms Fast-Newman algorithm and Girvan-Newman algorithm in terms of clustering accuracy and time complexity.
ISBN 9781479975754
Language eng
DOI 10.1109/PCCC.2014.7017060
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30073184

Document type: Conference Paper
Collection: School of Information Technology
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