SOM Network Based Clustering Analysis of Real Estate Enterprises

Abstract

For Real Estate industry which has many types of enterprises, how to carry on the effective clustering analysis has become a problem that needs to solve. This paper first theoretically elaborates the SOM network, and then pretreats the data with SOM network, which has the ability to deal with the high dimensional and complex data. Finally it uses the clustering function of SOM neural network to make clustering analysis and comparison of Real Estate companies which are listed in Shanghai and Shenzhen stock market. The clustering analysis results based on SOM are displayed in two-dimensional graphics, showing intuitively and comprehensively of the financial situation of each enterprise.

Share and Cite:

Zhu, J. and Liu, S. (2014) SOM Network Based Clustering Analysis of Real Estate Enterprises. American Journal of Industrial and Business Management, 4, 167-173. doi: 10.4236/ajibm.2014.43023.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Shanghai Real Estate Research Institute Comprehensive Research Study (2009) China’s Housing Price to Income Ratio Study: House Prices Fall, First-Tier Cities Are Still on the High Side.
[2] Tang, J.T. (2011) Commercial Bank Credit Risk under the Tight Real Estate Policy Thinking. Economic Research Guide.
[3] Kohonen, T. (1990) The Self-Organizing Maps. Proceedings of the IEEE, 78, 1464-1480.
http://dx.doi.org/10.1109/5.58325
[4] Li, G.Z. and Liu, T.Y. (2006) Feature Selection for Bagging of Support Vector Machines. Lecture Notes in Computer Science. Springer Verlag, Berlin, 271-277.
[5] Fitz, P. (1932) A Comparison of Ratios of Successful Industrial Enterprises with Those of Failed Firms. Certified Public Accountant, October, November, and December.
[6] Li, Q. and Yu, P. (2012) Main Methods Comparative Research on the Financial Crisis Prediction. Application of Statistics and Management, 7, 689-706.
[7] Bai, T. (2009) Commercial Bank Risk Management Practices. China Finance Publishing House, 1, 34-39.
[8] Xin, L. and Gao, J.L. (2012) Commercial Bank Risk Management Practices. Business Accounting.
[9] Xiao, Q., Qian, X.D. and Liao, H. (2012) Clustering Algorithm Analysis of Web Users with Dissimilarity and SOM Neural Networks. Journal of Software, 7, 11.
[10] Li, X.H. (2012) EVA Enterprise Early Warning Index System of Comprehensive Analysis of the Building. Modern Commerce. 32.
[11] Zhao, J.X. and Du, Z.P. (2009) Based on the Combination of Neural Network and Decision Tree of Credit Risk Assessment Model. Journal of Beijing Institute of Technology, 2009, 2.
[12] Wen, X.C. (2012) Enterprise Financial Crisis Warning Research Trend. Modern Commerce, 36.
[13] Boyacioglu, M.A., Kara, Y. and Baykan, O.K. (2009) Predicting Bank Financial Failures Using Neural Networks, Support Vector Machines and Multivariate Statistical Methods: A Comparative Analysis in the Sample of Savings Deposit Insurance Fund (SDIF) Transferred Banks in Turkey. Expert Systems with Applications, 36, 3355-3366.
[14] Wu, A.M. (2013) Credit Policy Effect of Real Estate and Commercial Bank Management Innovation Research. Southwest Finance.
[15] Nie, H. (2013) China’s Commercial Banks, Financial Crisis Early Warning Mechanism Research. Accounting Financial.
[16] Bao, X.Z., Wu, P. and Zhou, Y. (2012) Based on Clustering and Grey Correlation Analysis of the Financial EarlyWarning Index Screening Study. Chinese Certified Public Accountant.
[17] Ma, D.S., Ye, Z.R. and Hu, J.Z. (2013) Financial Audit in Our Country the Construction of Early Warning Index System and Index. Modern Finance and Economics, 2013, 1.
[18] Von, P.G. (2009) Asset Prices and Banking Distress: A Macroeconomic Approach. Journal of Financial Stability, 2009. http://dx.doi.org/10.1016/j.jfs.2009.01.001
[19] David, W.B. (2010) US Higher Education and Current Recession. International Higher Education, 55, 2.
[20] Zhang, R.Y., Zhang, A.W. and Huang, S.S. (2013) The Establishment of the Company’s Financial Risk Evaluation Index System. Finance and Accounting Monthly, 3.
[21] Parka, Y., Choi, J.K. and Zhang, A. (2009) Evaluating Competitiveness of Air Cargo Express Services. Transportation Research Part E: Logistics and Transportation Review, 45, 321-334.
http://dx.doi.org/10.1016/j.tre.2008.09.004

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.