Modified Approach for Classifying Multi- Dimensional Data-Cube Through Association Rule Mining for Granting Loans in Bank

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-33 Number-1
Year of Publication : 2016
Authors : Dr. K.Kavitha
  10.14445/22312803/IJCTT-V33P102

MLA

Dr. K.Kavitha "Modified Approach for Classifying Multi- Dimensional Data-Cube Through Association Rule Mining for Granting Loans in Bank". International Journal of Computer Trends and Technology (IJCTT) V33(1):9-13, March 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
In this paper, modified Approach for classifying Multi-dimensional data cube is constructed. It explores data cubes in large Multi-Dimensional Schema. Numerical and Nominal attributes are categorized based on Principal Component Analysis. Semantic relationships are identified by applying Multidimensional scaling. Additionally, AR is integrated for finding the inserting measures. Many algorithms have been proposed for applying Multi-dimensional schema. But still some difficulties to category wise the integrated rules. The proposed approach suggested a new idea for categorizing the rules by using bank loan detect. This method provides accurate prediction and consumes less time than existing method.

References
[1] M. Usman, R. Pears, and A. Fong, "Discovering diverse association rules from multidimensional schema," 2013.
[2] R. Pears, M. Usman, and A. Fong, "Data guided approach to generate multi-dimensional schema for targeted knowledge discovery," 2012.
[3] 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 on, 2010, pp. V5- 500-V5-503. [4] W.-Y. Chiang, "To mine association rules of customer values via a data mining procedure with improved model: An empirical case study," Expert Systems with Applications, vol. 38, pp. 1716- 1722, 2011.
[5] M. A. Domingues and S. O. Rezende, "Using taxonomies to facilitate the analysis of the association rules," arXiv preprint arXiv:1112.1734, 2011.
[6] T. Herawan and M. M. Deris, "A soft set approach for association rules mining," Knowledge-Based Systems, vol. 24, pp. 186-195, 2011.
[7] V. Kumar and A. Chadha, "Mining Association Rules in Student’s Assessment Data," International Journal of Computer Science Issues, vol. 9, pp. 211-216, 2012.
[8] C. Romero, J. R. Romero, J. M. Luna, and S. Ventura, "Mining Rare Association Rules from e-Learning Data," in EDM, 2010, pp. 171-180.
[9] H. Zhu and Q. Li, "An Algorithm Based on Predicate Path Graph for Mining Multidimensional Association Rules," in Proceedings of the 2012 International Conference on Information Technology and Software Engineering, 2013, pp. 783-791.
[10] C.-A. Wu, W.-Y. Lin, C.-L. Jiang, and C.-C. Wu, "Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining," Expert Systems with Applications, vol. 38, pp. 11011-11023, 2011.
[11] J. K. Chiang and H. Sheng-Yin, "Multidimensional data mining for healthcare service portfolio management," in Computer Medical CiiT International Journal of Data Mining and Knowledge Engineering, Vol 6, No 07, August 2014 Applications (ICCMA), 2013 International Conference on, 2013, pp. 1-8.
[12] P. Allard, S. Ferré, and O. Ridoux, "Discovering Functional Dependencies and Association Rules by Navigating in a Lattice of OLAP Views," in CLA, 2010, pp. 199-210.
[13] W. Moudani, M. Hussein, M. Moukhtar, and F. Mora- Camino, "An intelligent approach to improve the performance of a data warehouse cache based on association rules," Journal of Information and Optimization Sciences, vol. 33, pp. 601-621, 2012.
[14] M. Usman and S. Asghar, "An Architecture for Integrated Online Analytical Mining," Journal of Emerging Technologies in Web Intelligence, vol. 3, pp. 74-99, 2011.
[15] H. Uğuz, "A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm," Knowledge-Based Systems, vol. 24, pp. 1024-1032, 2011.
[16] W. Abdelbaki, S. B. Yahia, and R. B. Messaoud, "NAP-SC: A Neural Approach for Prediction over Sparse Cubes," in Advanced Data Mining and Applications, ed: Springer, 2012, pp. 340-352.
[17] J. Nahar, T. Imam, K. S. Tickle, and Y.-P. P. Chen, "Association rule mining to detect factors which contribute to heart disease in males and females," Expert Systems with Applications, 2012.
[18] P. Manda, F. McCarthy, and S. M. Bridges, "Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships," Journal of biomedical informatics, 2013.
[19] H. R. Qodmanan, M. Nasiri, and B. Minaei-Bidgoli, "Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence," Expert Systems with applications, vol. 38, pp. 288-298, 2011.
[20] C.-H. Weng and Y.-L. Chen, "Mining fuzzy association rules from uncertain data," Knowledge and Information Systems, vol. 23, pp. 129-152, 2010.
[21] N. Zbidi, S. Faiz, and M. Limam, "On mining summaries by objective measures of interestingness," Machine learning, vol. 62, pp. 175-198, 2006.
[22] K.Kala “DCAR: A Novel Approach for Datacubes Association Rule Algorithm in Multidimensional Schema” CiiT International Journal of Data Mining and Knowledge Engineering, Vol 6, No 07, August 2014

Keywords
Association Rules, Datacubes, Data Mining, Multidimensional Schema, Information Gain.