Semisupervised Clustering for Networks Based on Fast Affinity Propagation

Mu Zhu, Fanrong Meng and Yong Zhou

Mathematical Problems in Engineering, 2013, vol. 2013, 1-13

Abstract:

Most of the existing clustering algorithms for networks are unsupervised, which cannot help improve the clustering quality by utilizing a small number of prior knowledge. We propose a semisupervised clustering algorithm for networks based on fast affinity propagation (SCAN-FAP), which is essentially a kind of similarity metric learning method. Firstly, we define a new constraint similarity measure integrating the structural information and the pairwise constraints, which reflects the effective similarities between nodes in networks. Then, taking the constraint similarities as input, we propose a fast affinity propagation algorithm which keeps the advantages of the original affinity propagation algorithm while increasing the time efficiency by passing only the messages between certain nodes. Finally, by extensive experimental studies, we demonstrate that the proposed algorithm can take fully advantage of the prior knowledge and improve the clustering quality significantly. Furthermore, our algorithm has a superior performance to some of the state-of-art approaches.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2013/385265.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2013/385265.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:385265

DOI: 10.1155/2013/385265

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().