Integrating Association Rules with Decision Trees in Object-Relational Databases

International Journal of Computer Trends and Technology (IJCTT)          
© 2019 by IJCTT Journal
Volume-67 Issue-3
Year of Publication : 2019
Authors : Maruthi Rohit Ayyagari


MLA Style: Maruthi Rohit Ayyagari, "Integrating Association Rules with Decision Trees in Object-Relational Databases" International Journal of Computer Trends and Technology 67.3 (2019): 102-108.

APA Style: Maruthi Rohit Ayyagari, (2019). Integrating Association Rules with Decision Trees in Object-Relational Databases. International Journal of Computer Trends and Technology, 67(3), 102-108.

Research has provided evidence that associative classificationproduces more accurate results compared to other classification models. The Classification Based on Association (CBA) is one of the famous Associative Classification algorithms that generates accurate classifiers. However, current association classification algorithms reside externalto databases, which reduces the flexibility of enterprise analytics systems. Thispaper implementsthe CBA in Oracledatabase using two variant models—hardcoding the CBA in Oracle Data Mining (ODM)package and Integrating OracleApriori model with the OracleDecision tree model. We comparedthe proposed model performance with Naïve Bayes, Support Vector Machine, Random Forests, and Decision Tree over 18 datasets from UCI. Results showed that our models outperformed the original CBA model with 1% and is competitive to chosen classification models over benchmark datasets.

[1] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, “Advances in knowledge discovery and data mining,” 1996.
[2] F. Padillo, J. M. Luna, and S. Ventura, “Evaluating associative classification algorithms for Big Data,” Big Data Anal., vol. 4, no. 1, p. 2, 2019.
[3] A. B. Rodrigues et al., “Association between B-type natriuretic peptide and within-visit blood pressure variability,” Clin. Cardiol., vol. 41, no. 6, pp. 778–781, 2018.
[4] R. Agrawal, R. Srikant, and others, “Fast algorithms for mining association rules,” in Proc. 20th int. conf. very largedata bases, VLDB, 1994, vol. 1215, pp. 487–499.
[5] K. Ali, S. Manganaris, and R. Srikant, “Partial Classification Using Association Rules.,” in KDD, 1997, vol. 97, pp. p115--118.
[6] B. Liu, W. Hsu, Y. Ma, and others, “Mining association rules with multiple minimum supports,” in KDD, 1999, vol. 99, pp. 337–341.
[7] J. R. Quinlan, “Induction of decision trees,” Mach. Learn., vol. 1, no. 1, pp. 81–106, 1986.
[8] J. R. Quinlan, C4. 5: programs for machine learning. Elsevier, 2014.
[9] W. W. Cohen, “Fast effective rule induction,” in Machine Learning Proceedings 1995, Elsevier, 1995, pp. 115–123.
[10] J. R. Quinlan and R. M. Cameron-Jones, “FOIL: A midterm report,” in European conference on machine learning, 1993, pp. 1–20.
[11] J. Alwidian, B. H. Hammo, and N. Obeid, “WCBA: Weighted classification based on association rules algorithm for breast cancer disease,” Appl. Soft Comput., vol. 62, pp. 536–549, 2018.
[12] B. L. W. H. Y. Ma and B. Liu, “Integrating classification and association rule mining,” in Proceedings of the fourth international conference on knowledge discovery and data mining, 1998, pp. 24–25.
[13] W. L. J. H. J. Pei and others, “CMAR: Accurate and efficient classification based on multiple class-association rules,” ICDM-2004, 2001.
[14] F. Thabtah, P. Cowling, and Y. Peng, “MCAR: multi-class classification based on association rule,” in The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 2005., 2005, p. 33.
[15] F. A. Thabtah, P. Cowling, and Y. Peng, “MMAC: A new multi-class, multi-label associative classification approach,” in null, 2004, pp. 217–224.
[16] X. Yin and J. Han, “CPAR: Classification based on predictive association rules,” in Proceedings of the 2003 SIAM International Conference on Data Mining, 2003, pp. 331–335.
[17] Z. Ali, R. Ahmad, M. N. Akhtar, Z. H. Chuhan, H. M. Kiran, and W. Shahzad, “Empirical Study of Associative Classifiers on Imbalanced Datasets in KEEL,” in 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA), 2018, pp. 1–7.
[18] M. Attaran and J. Woods, “Cloud Computing Technology: A Viable Option for Small and Medium-Sized Businesses.,” J. Strateg. Innov. Sustain., vol. 13, no. 2, 2018.
[19] P. Tamayo et al., “Oracle data mining,” in Data mining and knowledge discovery handbook, Springer, 2005, pp. 1315–1329.
[20] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification and scene analysis, vol. 3. Wiley New York, 1973.
[21] Y. Shao, B. Liu, S. Wang, and G. Li, “A novel software defect prediction based on atomic class-association rule mining,” Expert Syst. Appl., vol. 114, pp. 237–254, 2018.
[22] G. Chen, H. Liu, L. Yu, Q. Wei, and X. Zhang, “A new approach to classification based on association rule mining,” Decis. Support Syst., vol. 42, no. 2, pp. 674–689, 2006.
[23] M.-L. Antonie and O. R. Zaïane, “An associative classifier based on positive and negative rules,” in Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, 2004, pp. 64–69.
[24] W. Li, “Classification based on multiple association rules,” Simon Fraser University, 2001.
[25] E. Baralis and P. Garza, “A lazy approach to pruning classification rules,” in 2002 IEEE International Conference on Data Mining, 2002. Proceedings., 2002, pp. 35–42.
[26] E. Baralis, S. Chiusano, and P. Garza, “On support thresholds in associative classification,” in Proceedings of the 2004 ACM symposium on Applied computing, 2004, pp. 553–558.
[27] J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” in ACM sigmod record, 2000, vol. 29, no. 2, pp. 1–12.
[28] Z. P. Ogihara, M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, “New algorithms for fast discovery of association rules,” in In 3rd Intl. Conf. on Knowledge Discovery and Data Mining, 1997.
[29] A. Anitha and F. Jebamalar, “Predicting Dengue Using Fuzzy Association Rule Mining,” Int. J. Comput. Trends Technol., vol. 67, no. 3, 2019.
[30] H. Buttar and R. Kaur, “Association Technique in Data Mining and Its Applications,” Int. J. Comput. Trends Technol. (IJCTT)-volume4Issue4--April 2013, 2013.
[31] K. Chari and M. Agrawal, “Impact of incorrect and new requirements on waterfall software project outcomes,” Empir. Softw. Eng., vol. 23, no. 1, pp. 165–185, 2018.
[32] A. Nofal and S. Bani-Ahmad, “Classification based on association-rule mining techniques: a general survey and empirical comparative evaluation,” Ubiquitous Comput. Commun. J., vol. 5, no. 3, 2010.
[33] A. Asuncion and D. Newman, “UCI machine learning repository.” 2007.
[34] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, Nov. 2011.
[35] F. Jǐŕı and T. Kliegr, “Classification based on Associations (CBA) - A Performance Analysis,” in RuleML+RR, 2018.

Associative Classification; Decision Trees; CBA; Oracle;Data Mining;Database