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IJSTR >> Volume 4 - Issue 6, June 2015 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Pattern Analysis On Banking Dataset

[Full Text]

 

AUTHOR(S)

Amritpal Singh, Amrita Kaur, Jasmeet Kaur, Ramandeep Singh, Shipra Raheja

 

KEYWORDS

Keyword: Data Mining, Business, Customer acquisition, retention management, Marketing, Fraud and Risk Management

 

ABSTRACT

Abstract: Everyday refinement and development of technology has led to an increase in the competition between the Tech companies and their going out of way to crack the system andbreak down. Thus, providing Data mining a strategically and security-wise important area for many business organizations including banking sector. It allows the analyzes of important information in the data warehouse and assists the banks to look for obscure patterns in a group and discover unknown relationship in the data.Banking systems needs to process ample amount of data on daily basis, related to customer information, their credit card details, limit and collateral details, transaction details, risk profiles, Anti Money Laundering related information, trade finance data. Thousands of decisions,based on the related data, are taken in a bank daily. This paper analyzes the banking dataset in the weka environment for the detection of interesting patterns based on its applications ofcustomer acquisition, customer retention management, and marketing and management of risk, fraudulence detections.

 

REFERENCES

[1] Ian Davidson. “Understanding K-Means Non-hierarchical Clustering”, Univ. of California publications,2014.http://academic.research.microsoft.com/Keyword/21448/k-means-algorithm

[2] A. Floares., A. Birlutiu. “Decision Tree Models for Developing Molecular Classifiers for Cancer Diagnosis”, WCCI 2012 IEEE World Congress on Computational Intelligence June, 10-15, 2012 - Brisbane, Australia.

[3] K. Chitra, B.Subashini, “Customer Retention in Banking Sector using Predictive Data Mining Technique”, International Conference on Information Technology, Alzaytoonah University, Amman, Jordan, 2011.www.zuj.edu.jo/conferences/icit11/paperlist/Papers/

[4] Xindong Wu, Xingquan Zhu , Gong-Qing Wu. ,”Knowledge and Data Engineering” , IEEE Transactions on (Volume:26 , Issue: 1 ) ,2011. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=69

[5] S.P. Deshpande, Dr. V.M. Thakare,”System and Applications: A Review Data Mining “, International Journal of Distributed and Parallel system, September2010.

[6] D. Muraleedharan, “Modern Banking: Theory and Practice”, PHI Learning private Limited, 2009.

[7] Rajanish Dass, "Data Mining in Banking and Finance: A Note for Bankers", Indian Institute of Management Ahmadabad, 2008.

[8] Dr.Madan Lal Bhasin, “Data Mining: A Competitive Tool in the Banking and Retail Industries”, the Chartered Accountant October, 2006.

[9] Hillol Kargupta, Anupam Joshi, Krishnamoorthy Siva Kumar, Yelena Yesha, "Data Mining: Next Generation Challenges and Future Directions", Publishers: Prentice-Hall of India, Private Limited, 2005.

[10] I.H. and Frank, “Data Mining: Practical machine learning tools and techniques. “. 2nd edition Morgan Kaufmann, San Francisco, 2005.

[11] M. De Martino, A. Bertone, R. Albertoni, H. Hauska, U. Demsar, M. Dunkars. :”Technical Report of Data Mining”, INVISIP IST-2000-29640, Information Visualisation for Site Planning, WP No2: Technology Analysis, D2.2, 2002.

[12] S. S. Kaptan, N S Chobey, "Indian Banking in Electronic Era", Sarup and Sons, Edition 2002.

[13] S.S.Kaptan, “New Concepts in Banking”, Sarup and Sons, Edition, 2002

[14] M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, "New Algorithms for Fast Discovery of Association Rules", Proc. 3rd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'97, Newport Beach, CA), 283-296 AAAI Press, Menlo Park, CA, USA 1997.

[15] Drummond, C. and Holte, “An alternative to ROC representation.” Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Publishers, San Mateo, CA.

[16] Aggarwal, R., T. Imielin´ ski, and A. Swami, "Mining association rules between sets of items in large databases". In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD ’93, New York, NY, USA, pp. 207–216. ACM.