The Research on Label Propagation Algorithm and Improvement Based on Local Information Mechanism

Yong Jiang Deng

doi:10.4028/www.scientific.net/AMR.717.803

Paper Titles

Application of the Multimedia Teaching in Martial Arts Training
p.784

A Multi-Server Erasure Code Algorithm Based on the Degree of Restriction
p.789

Design of Hardware Trojan Applying on Cryptogrammic-Board for PC
p.794

An Improved CAMSHIFT Tracking Algorithm Applying on Surveillance Videos
p.797

The Research on Label Propagation Algorithm and Improvement Based on Local Information Mechanism
p.803

A Study on Rumor Propagation Model on Micro-Blogging Platforms
p.808

The Practice of Constructing the Characteristic Database of the Library under the Network Environment
p.812

Data Collection for Medical Data Warehouse Research
p.816

The Study of Construction Bill of Quantities Pricing System Based on Software Reuse-Based
p.820

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 717The Research on Label Propagation Algorithm and...

The Research on Label Propagation Algorithm and Improvement Based on Local Information Mechanism

Abstract:

Heuristic clustering method based on local information is introduced, and then the label propagation method based on local information is summarized, and the problem of the iterative process and using a random strategy to select a node belongs to the cluster structure are analyzed. Label propagation algorithm base on the similarity of node attributes is improved. At last, the experiments are used to help to discover the efficient and availability of the algorithm, and put the algorithm into preliminary application.

You might also be interested in these eBooks

Key Engineering Materials and Computer Science II

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 717)

Pages:

803-807

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.717.803

Citation:

Cite this paper

Online since:

July 2013

Authors:

Yong Jiang Deng

Keywords:

Information, Label Propagation, Mechanism

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] LEE K, KIM DW, LEE D,et al. Improving support vector data description using local density degree[J].Pattem Recognition,2005,38(10):1768-1771

DOI: 10.1016/j.patcog.2005.03.020

Google Scholar

[2] LEE K,KIM D W,LEE K H,et al. Density-induced support vector data description[J]. IEEE Trans on Neural Networks,2007,18(1):284-289

DOI: 10.1109/tnn.2006.884673

Google Scholar

[3] [16] Liu Sheng, Zhu Yuquan, Sun Jinjin. Algorithm based on kernel density relative to that of the SVDD classification algorithm [J]. Application Research of computers, 27 (5): 1694-16962010

Google Scholar

[4] R. Smith,A. Bivens, M. Embrechts, C. Palagiri, and B. Szymanski. Clustering approaches for anomaly based intrusion detection. In Proceedings of Intelligent Engineering Systems through Artificial Neural Networks, pages 579–584. ASME Press, (2002)

Google Scholar

[5] V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection:A survey. ACM Computing Surveys, 41(3):Article 15, July (2009)

DOI: 10.1145/1541880.1541882

Google Scholar

[6] M.M. Breunig, H.P. Kriegel, R.T. Ng, and J. Sander.LOF: identifying density-based local outliers. In SIGMOD 2000, pages 93–104, Dallas, USA, May (2000)

DOI: 10.1145/335191.335388

Google Scholar

[7] Bo Liu, Jie Yin, Yanshan Xiao, Longbing Cao and Philip S. Exploiting Local Data Uncertainty to Boost Global Outlier Detection[J].IEEE International Conference on Data Mining,(2010)

DOI: 10.1109/icdm.2010.10

Google Scholar

[8] C. X. Ling, J. Huang, and H. Zhang. Auc: a statistically consistent and more discriminating measure than accuracy. In IJCAI 2003, pages 519–526, Acapulco, Mexico, August (2003)

Google Scholar