An Improved K-means Clustering Algorithm for Complex Networks
- Hao Li, Haoxiang Wang, Zengxian Chen
- Corresponding Author
- Hao Li
Available Online March 2015.
- https://doi.org/10.2991/iset-15.2015.24How to use a DOI?
- Complex networks, K-means, Clustering, Node importance
- 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.
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 SN - 2352-538X UR - https://doi.org/10.2991/iset-15.2015.24 DO - https://doi.org/10.2991/iset-15.2015.24 ID - Li2015/03 ER -