ME  Vol.3 No.5 , September 2012
An Improved Fuzzy ISODATA Algorithm for Credit Risk Assessment of the EIT Enterprises
Abstract: We proposed an improved fuzzy ISODATA algorithm for credit risk assessment of the emerging information technol-ogy enterprise in this paper. Firstly, as the uncertainty of the EIT enterprise is relatively large, we set a reference sample and an initial clustering center matrix so that we overcame the shortcomings of traditional ISODATA algorithm and improved the reliability of fuzzy clustering analysis. Secondly, we proposed the steps of evaluating the EIT enterprises’ credit risk with improved fuzzy ISODATA algorithm. Last but not least, we assessed 10 EIT enterprises’ credit risk of a certain city, which proved the effectiveness and operability.
Cite this paper: Z. Zhou, "An Improved Fuzzy ISODATA Algorithm for Credit Risk Assessment of the EIT Enterprises," Modern Economy, Vol. 3 No. 5, 2012, pp. 686-689. doi: 10.4236/me.2012.35088.

[1]   Z. F. Zhou, et al., “The Evolution and Evaluation of Credit Risk for Emerging Technology Companies,” Sci- ence Press, Beijing, 2010. (in Chinese)

[2]   J. Yu, Z. Zhou, et al., “The Ordering Structure of Evalua- tion Indexes Sequences for the EIT Project,” Journal of Emerging Trends in Computing and Information Sciences, Unpublished.

[3]   J. Yu, Z. Zhou, et al., “Study on Evaluation for EIT Project Based on Membership Cloud Gravity Center,” ICISE 2011.

[4]   M. Lundy, “Cluster Analysis in Credit Scoring, Credit Scoring and Credit Control,” Oxford University Press, New York, 1993.

[5]   H. Lin and M. Xia, “Enterprise Credit Rating of Theory and Practice,” Foreign Economic and Trade University Press, Beijing, 2003. (in Chi-nese)

[6]   J. C. Bezdek, “Pattern Recognition with Fuzzy Ob-jective Function Algorithms,” Plenum Press, New York, 1981.

[7]   S. Chen, J. Chen and X. Wang, “The Fuzzy Set Theory and Its Application,” Science Press, Beijing, 2005. (in Chinese)

[8]   Z. Pawlak, “Rough Set: Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publishers, Dordrecht, 1991.

[9]   X. Xu, et al., “Based on the Fuzzy Toolbox and Rosetta’s Rough Set Data Mining,” Microcomputer Information, Vol. 23, No. 18, 2007, pp. 174-178.

[10]   Y. Zhang, Z. Zhou and H. Tang, “An Selection Method of the ETF’s Credit Risk Evaluation Indicators,” ICCS 2008, Kraków, 2008.