Proceedings of the First International Conference on Information Science and Electronic Technology

An Improved K-means Clustering Algorithm for Complex Networks

Authors
Hao Li, Haoxiang Wang, Zengxian Chen
Corresponding Author
Hao Li
Available Online March 2015.
DOI
https://doi.org/10.2991/iset-15.2015.24How to use a DOI?
Keywords
Complex networks, K-means, Clustering, Node importance
Abstract
The exploration about cluster structure in Complex Networks is crucial for analyzing and understanding Complex Networks. K-means algorithm is a widely used clustering algorithm. In this paper, a novel algorithm is proposed based on K-means. Considering, Complex Networks obeys Power-law Degree Distribution, this improved algorithm chooses nodes with high importance as the initial clustering centroids, and uses the distance to these key nodes as clustering measurement. The experiments prove that the new algorithm can conduct accurate clustering with acceptable performance.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
First International Conference on Information Science and Electronic Technology (ISET 2015)
Part of series
Advances in Computer Science Research
Publication Date
March 2015
ISBN
978-94-62520-50-9
DOI
https://doi.org/10.2991/iset-15.2015.24How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Hao Li
AU  - Haoxiang Wang
AU  - Zengxian Chen
PY  - 2015/03
DA  - 2015/03
TI  - An Improved K-means Clustering Algorithm for Complex Networks
BT  - First International Conference on Information Science and Electronic Technology (ISET 2015)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/iset-15.2015.24
DO  - https://doi.org/10.2991/iset-15.2015.24
ID  - Li2015/03
ER  -