1. 1. G.K. Gupta, “Introduction to Data Mining with Case Studies”, Prentice-Hall of India Pvt. Limited, 2006.
   2. Saravanan Suba and Christopher T, “A Study on Milestones of Association Rule Mining Algorithms in Large Databases”, International Journal of Computer Applications, Vol. 47, No. 3, pp. 12-19, 2012.
   3. Ya-Han Hu and Yen-Liang Chen, “Mining Association Rules with Multiple Minimum Supports: A New Mining Algorithm and A Support Tuning Mechanism”, Decision Support Systems, Vol. 42, No. 1, pp. 1-24, 2006.
   4. S. Shankar and T. Purusothaman, “Utility Sentient Frequent Itemset Mining and Association Rule Mining: A Literature Survey and Comparative Study”, International Journal of Soft Computing Applications, No. 4, pp. 81-95, 2009.
   5. N.P. Gopalan and B. Sivaselvan, “Data Mining Techniques and Trends”, PHI Learning, 2009.
   6. Ashoka Savasere, Edward Omiecinski and Shamkant B. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases”, Proceedings of the 21st International Conference on Very Large Data Bases, pp. 432-444, 1995.
   7. R. Agrawal, T. Imielinski and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases”, Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207-216, 1993.
   8. M.J. Zaki and C.J. Hsiao, “CHARM: An Efficient Algorithm for Closed Association Rule Mining”, Technical Report 99-10, Department of Computer Science, Rensselaer Polytechnic Institute, 1999.
   9. Yin-Ling Cheung and A.W.-C. Fu, “Mining Frequent Itemsets without Support Threshold: with and without Item Constraints”, IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 9, pp. 1052-1069, 2004.
   10. R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules”, Proceedings of 20th International Conference on Very Large Data Bases, pp. 487-499, 1994.
   11. Jong Soo Park, Ming-Syan Chen and Philip S. Yu, “Using a Hash- Based Method with Transaction Trimming and Database Scan Reduction for Mining Association Rules”, IEEE Transactions on Knowledge and Data Engineering, Vol. 9, No. 5, pp. 813-825, 1997.
   12. H. Toivonen, “Sampling Large Databases for Association Rules”, Proceedings of the 22th International Conference on Very Large Data Bases, pp. 134-145, 1996.
   13. Jong Soo Park, Ming-Syan Chen and Philip S. Yu, “An Effective Hash Based Algorithm for Mining Association Rules”, Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, pp. 175-186, 1995.
   14. Jiawei Han, Jian Pei and Yiwen Yin, “Mining Frequent Patterns without Candidate Generation”, Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1-12, 2000.
   15. Mohammed Al-Maolegi1, Bassam Arkok, “An Improved Apriori Algorithm for Association Rules”, International Journal on Natural Language Computing, Vol. 3, No. 1, pp. 21-29, 2014.
   16. R. Srikant and R. Agrawal, “Mining Generalized Association Rules”, Future Generation Computer Systems, Vol. 13, No. 2-3, pp. 161-180, 1997.
   17. S. Sunil Kumar, S. Shyam Karanth, K.C. Akshay, Ananth Prabhu and M. Bharathraj Kumar, “Improved Apriori Algorithm based on Bottom Up Approach using Probability and Matrix”, International Journal of Computer Science Issues, Vol. 9, No. 2, pp. 232-246, 2012.
   18. Z. Souleymane, P.F. Viger, J.C.-W. Lin, C.-W. Wu and V.S. Tseng, “EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining”, Proceedings of 14th Mexican International Conference on Artificial Intelligence, pp. 530-546, 2015.
   19. R. Ahirwal, N.K. Kori and Y.K. Jain, “Improved Data Mining Approach to Find Frequent Itemset using Support Count Table”, International Journal of Emerging Trends & Technology in Computer Science, Vol. 1, No. 2, pp. 195-201, 2012.