Pertaining the Concept of Risk Evaluation and Prediction for Multi-Dimensional Clustering

International Journal of Computer Trends and Technology (IJCTT)          
© 2016 by IJCTT Journal
Volume-32 Number-1
Year of Publication : 2016
Authors : Dr. K.Kavitha
DOI :  10.14445/22312803/IJCTT-V32P103


Dr. K.Kavitha "Pertaining the Concept of Risk Evaluation and Prediction for Multi-Dimensional Clustering". International Journal of Computer Trends and Technology (IJCTT) V32(1):14-16, February 2016. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Data mining technique has some major improvement in the field of knowledge discovery clustering is an important techniques to group the similar items without advance knowledge. Risk assessment is an important task of passport sanction through E_corner. Risk evaluation process is used to identify the applicant’s behaviour. Risk evaluation and prediction is done based on decision making approach. This method allows the user to generate the risk percentage can be sanctioned or not. This paper concentrates mainly the concept of multi-dimensional data clustering for Risk evaluation and prediction.

[1] G.Francesca, “A Discrete-Time Hazard Model for Loans: Some evidence from Italian Banking system”, American Journal of Applied Sciences, vol. 9, p. 1337, 2012
[2]D.Zakrzewska, “On integrating unsupervised and supervised classification for credit risk evaluation”, Information Technology and Control, Vol. 36, pp. 98-102, 2007.
[3]M.L.Bhasin, “Data Mining: A Competitive tool in the Banking and Retail Industries” Banking and Finance, vol 588,2006
[4] M.Usman, R.Pears and A.Fong, “Discovering diverse Association Rules from Multidimensional Schema,”,2013
[5] R.Pears,M.Usman and A.Fong, “Data guided approach to generate Multi0Dimensional schema for targeted Knowledge discovery”, 2012.
[6] G.Liu, H.Jiang, R.Geng and H.Li, “Application of Multidimensional association rules in personal financial services” in Computer design and Applications(ICCDA), 2010 International Conference ,pp V5-500-503
[7] W.Y.Chiang “To mine association rules of Customer values via data mining procedure with improved model:An empirical case study’Expert Systems with Applications, Vol 38, pp 1716- 1722,2011
[8] T.Herawan and M.M.Deris, “A soft set approach for association rule mining Knowledge based Systems”, vol 24, pp 186-195,2011.
[9]A. Ghatge and P. Halkarnikar, "Ensemble Neural Network Strategy for Predicting Credit Default Evaluation."
[10]S. Purohit and A. Kulkarni, "Credit evaluation model of loan proposals for Indian Banks," in Information and Communication Technologies (WICT), 2011 World Congress on, 2011, pp. 868-873.
[11]D. Zakrzewska, "On integrating unsupervised and supervised classification for credit risk evaluation," Information Technology and Control, vol. 36, pp. 98-102, 2007.
[12]M. L. Bhasin, "Data Mining: A Competitive Tool in the Banking and Retail Industries," Banking and finance, vol. 588, 2006.
[13]N. ?kizler and H. A. Guvenir, "Mining interesting rules in bank loans data," in Proceedings of the Tenth Turkish Symposium on Artificial Intelligence and Neural Networks, 2001.
[14]J. Nassali, "A Loan Assessment System for Centenary Rural Development Bank," 2005.
[15]T. Jacobson and K. Roszbach, "Bank lending policy, credit scoring and value-at-risk," Journal of banking & finance, vol. 27, pp. 615-633, 2003.
[16]G. Kabir, I. Jahan, M. H. Chisty, and M. A. A. Hasin, "Credit Risk Assessment and Evaluation System for Industrial Project."
[17]B. Bodla and R. Verma, "Credit Risk Management Framework at Banks in India," ICFAI Journal of Bank Management, Feb2009, vol. 8, pp. 47-72, 2009.
[18]R. Raghavan, "Risk Management in Banks," CHARTERED ACCOUNTANT-NEW DELHI-, vol. 51, pp. 841-851, 2003.
[19]M. A. Karaolis, J. A. Moutiris, D. Hadjipanayi, and C. S. Pattichis, "Assessment of the risk factors of coronary heart events based on data mining with decision trees," Information Technology in Biomedicine, IEEE Transactions on, vol. 14, pp. 559-566, 2010.

Risk Evaluation, Prediction, Association Rule, Interesting Measures, Feature Extraction.