EVALUATION OF ASSOCIATION RULES BASED ON CERTAINTY FACTOR: AN APPLICATION ON DIABETES DATA SET

Abstract

Data mining is the process of discovering useful information that has not been previously revealed from large amounts of data. Association rules mining is one of the most important techniques used in data mining and artificial intelligence. The first research in the association rules was to find relationships between different products in the customer transaction database and customer purchase models. Based on these relationships, researchers have begun to expand the field of data mining. One of these areas is the application of the rules of association in the field of medicine. Thus, through these applications, the relationship of various features in medical data can be discovered, and the findings obtained can aid medical diagnosis. Support and confidence are the two primary measures employed in the evaluation of association rules. The rules obtained with these two values are often correct ; however, they are not strong rules. For this reason, there are many interestingness measures proposed to achieve stronger rules. Most of the rules, especially with a high support value, are misleading. For this reason, there are many interestingness measures proposed to achieve stronger rules. This study aims to establish strong association rules with variables in the open-sourced diabetes data set. In the current study, the Apriori algorithm was used to obtain the rules. As a result of the analysis, only 52 confidence and support criteria were taken into consideration. For more powerful rules, certainty factor was used as one of the interestingness measures proposed in the literature, and it was concluded that only 39 of these rules were strong as a result of the analysis.

Keywords

• Data Mining,
• Association Rules Mining,
• Apriori Algorithm
• Interestingness Measures

References

1. M.-S. Chen, J. Han, and P. S. Yu, "Data mining: an overview from a database perspective," IEEE Transactions on Knowledge and data Engineering, vol. 8, no. 6, pp. 866-883, 1996.
2. M. Ilayaraja and T. Meyyappan, "Mining medical data to identify frequent diseases using Apriori algorithm," in 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, 2013, pp. 194-199: IEEE.
3. W.-J. Zhang, D.-L. Ma, and B. Dong, "The automatic diagnosis system of breast cancer based on the improved Apriori algorithm," in 2012 International Conference on Machine Learning and Cybernetics, 2012, vol. 1, pp. 63-66: IEEE.
4. D. Dua and C. J. C. a. C. U. Graff, "UCI machine learning repository [http://archive. ics. uci. edu/ml]. https://archive. ics. uci. edu/ml/datasets," 2019.
5. S. Kumar and N. Joshi, "Rule power factor: a new interest measure in associative classification," Procedia Computer Science, vol. 93, pp. 12-18, 2016.
6. S. Rao and P. Gupta, "Implementing Improved Algorithm Over APRIORI Data Mining Association Rule Algorithm 1," 2012.
7. F. Berzal, I. Blanco, D. Sánchez, and M.-A. Vila, "Measuring the accuracy and interest of association rules: A new framework," Intelligent Data Analysis, vol. 6, no. 3, pp. 221-235, 2002.
8. D. Jain and S. Gautam, "Implementation of apriori algorithm in health care sector: a survey," International Journal of Computer Science and Communication Engineering, vol. 2, no. 4, pp. 22-8, 2013.

9. J. Manimaran and T. Velmurugan, "Analysing the quality of association rules by computing an interestingness measures," Indian Journal of Science and Technology, vol. 8, no. 15, pp. 1-12, 2015.
10. O. Başak, B. Uğur, and M. K. SAMUR, "Kulak Burun Boğaz Epikriz Notlarından Birliktelik Kurallarının Çıkartılması," 2009.
11. S. Kotsiantis, D. J. G. I. T. o. C. S. Kanellopoulos, and Engineering, "Association rules mining: A recent overview," vol. 32, no. 1, pp. 71-82, 2006.
12. W. van Melle, E. H. Shortliffe, and B. G. J. R.-b. e. s. T. M. e. o. t. S. H. P. P. Buchanan, "EMYCIN: A knowledge engineer’s tool for constructing rule-based expert systems," pp. 302-313, 1984.
13. F. Berzal, I. Blanco, D. Sánchez, and M.-A. J. I. D. A. Vila, "Measuring the accuracy and interest of association rules: A new framework," vol. 6, no. 3, pp. 221-235, 2002.