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

Yong Jiang Deng

doi:10.4028/www.scientific.net/AMM.422.279

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 422The Research on Label Propagation Algorithm and...

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

Abstract:

First, heuristic clustering method based on local information is introduced, second, the label propagation method based on local information is summarized, and then problem of the iterative process and a random strategy to select a node that 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 discover the efficient and availability of the algorithm, and put the algorithm into preliminary application.

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Periodical:

Applied Mechanics and Materials (Volume 422)

Pages:

279-284

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.422.279

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Online since:

September 2013

Authors:

Yong Jiang Deng

Keywords:

Information, Label Propagation, Mechanism

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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] 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