Open Access Open Access  Restricted Access Subscription or Fee Access

DCAR: A Novel Approach for Datacubes Association Rule Algorithm in Multidimensional Schema

K. Kala


In this paper a novel approach is proposed for the exploration of datacubes in large multidimensional schema. The attributes are categorized as nominal and numerical attributes. The numeric attributes are ranked based on Principal Component Analysis (PCA) method; the nominal attributes are ranked based on Information Gain. Here, multidimensional scaling is applied to find the semantic relationships among the values for each nominal attribute. The informative datacubes are constructed based on the highly ranked facts and dimensions. Importance measure is calculated to find the interestingness measures among the association rules. The interested rules are classified based on IRCA  algorithm. The proposed algorithm categorizes the interested rules into highly interested, medium interested and low interested rules. The proposed approach selects a subset of informative dimensions and facts from an initial set of dataset. The results are tested on real world datasets taken from the Bank Loan dataset. The proposed method provides accurate prediction and consumes less computing time when compared with the existing method.


Association Rules, Datacubes, Data Mining, Multidimensional Schema, Information Gain, Principal Component Analysis (PCA).

Full Text:



M. Usman, R. Pears, and A. Fong, "Discovering diverse association rules from multidimensional schema," 2013.

R. Pears, M. Usman, and A. Fong, "Data guided approach to generate multi-dimensional schema for targeted knowledge discovery," 2012.

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.

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.

M. A. Domingues and S. O. Rezende, "Using taxonomies to facilitate the analysis of the association rules," arXiv preprint arXiv:1112.1734, 2011.

T. Herawan and M. M. Deris, "A soft set approach for association rules mining," Knowledge-Based Systems, vol. 24, pp. 186-195, 2011.

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.

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.

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.

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.

J. K. Chiang and H. Sheng-Yin, "Multidimensional data mining for healthcare service portfolio management," in Computer Medical Applications (ICCMA), 2013 International Conference on, 2013, pp. 1-8.

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.

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.

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.

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.

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.

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.

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.

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.

C.-H. Weng and Y.-L. Chen, "Mining fuzzy association rules from uncertain data," Knowledge and Information Systems, vol. 23, pp. 129-152, 2010.

N. Zbidi, S. Faiz, and M. Limam, "On mining summaries by objective measures of interestingness," Machine learning, vol. 62, pp. 175-198, 2006.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.