Auto-Clustering Algorithm for Heterogeneous Information Network Using Improved Particle Swarm Optimization

Abstract:

NLM (National Library of Medicine) is one heterogeneous information network, which mixes scholars, MeSH (Medical Subject Headings), journals and research domains. Mining the rules and knowledge concealed among NLM is one hot topic in social computing applications. In this paper, an auto-clustering algorithm for NLM was proposed to uncover the embedded knowledge concerned with medical scholars and medical journals. This algorithm adopts particle swarm optimization (PSO) as iterating algorithm to automatically cluster scholars and journals. In addition, our algorithm utilizes the mutation in genetic algorithm (GA) to overcome local optimization, which is one outstanding bottle neck in various heuristic methods. The effectiveness of our algorithm is demonstrated by applying it to a subset of NLM.

• Periodical: Applied Mechanics and Materials (Volumes 239-240)
• Pages: 1448-1455
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.239-240.1448
• Online since: December 2012
• Authors: Chang Ping Liu, Yang Liu, Jia Shi Chen
• Keywords: Clustering Algorithm, Date Mining, Genetic Algorithm (GA), Knowledge Recovery, Particle Swarm Optimization (PSO)

References

[1] U.S. National Library of Medicine. "About the National Library of Medicine" [DB/OL].[2011-7-19]. http://www.nlm.nih.gov/about/index. html.

[2] U.S. National Library of Medicine. "PubMed home". [DB/OL].[2011-7-19]. http://www.ncbi.nlm.nih.gov/pubmed/.

[3] U.S. National Library of Medicine. "Medical Subject Headings (MeSH®)".[DB/OL].[2011-7-19]. http://www.nlm.nih.gov/pubs/fact sheets/mesh.html.

[4] D. Watts, S. Strogatz, Collective Dynamics of Small World Networks, Nature, 1998, 363: 202-204.

[5] J. Kennedy, R. C. Eberhart, and Y. Shi, Swarm Intelligence.San Francisco: Morgan Kaufmann, CA, 2001.

[6] Russel C. Eberhart, Yuhui Shi, Particle Swarm Optimization: Developments, Applications and Resources, 81-86.

[7] M.A. Abido, Optimal design of power-system stabilizers using particle swarm optimization. IEEE transactions on energy conversion, vol 17, no 3, september 2002. 406-413

DOI: 10.1109/tec.2002.801992

[8] Zwe-Lee Gaing, A Particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE transactions on enerty conversion, vol 19, no 2, june 2004. 384-391

DOI: 10.1109/tec.2003.821821

[9] Carlos A. Coello Coello, Gregorio Toscano Pulido and Maximino Salazar Lechuga, Handing multiple objects with particle swarm optimization, IEEE Transactions on evolutionary computation, vol 8, no 3, june 2004, 256-279.

DOI: 10.1109/tevc.2004.826067

[10] Jinn-Tsong Tsai, Tung-Kuan Liu and Jyh-Horng Chou, Hybrid Taguchi-genetic algorithm for global numberical optimization. IEEE transactions on evolutionary computation, vol 8 no 4 august 2004, 365-377.

DOI: 10.1109/tie.2006.874280

[11] Cezary Z. Janikow, A knowledge-intensive genetic algorithm for supervised learnging. Maching learning, 1993, 189-228.

[12] Francois spitz, Crole Herkenne, Michael A Morris and Denis Duboute, Inversion-induced disruption of the Hoxd cluster leads to the partition of regulatory landscapes. Nature Genetics volume 37, number 8, august 2005, 889-893

DOI: 10.1038/ng1597

[13] F. Murtagh, A survey of recent advances in hierarchical clustering algorithms. The computer journal, vol 26, no 4, 1983, 354-359

DOI: 10.1093/comjnl/26.4.354

[14] S. Mancoridis, B.S. Mitchell, Y.Chen and E.R. Gansner, Bunch: A clustering tool for the recovery and maintenance of software system structures.

DOI: 10.1109/icsm.1999.792498

[15] Yizhou Sun, Yintao Yu and Jiawei Han, Ranking-based clustering of heterogeneous information networks with star network schema. KDD 2009, 797-805.

[16] Ron Bekkerman, Ran El-Yaniv and Andrew McCallum, Multi-way distributional clustering via pairwise interactions. Proceedings of the 22th International conference on Machine Learning, 2005, 41-48.

DOI: 10.1145/1102351.1102357

[17] U.S. National Library of Medicine. "FTP Directory" [DB/OL].[2011-7-19]. ftp://ftp.nlm.nih.gov/nlmdata.