Association rule mining is a procedure which is meant to find frequent patterns from data sets found in various kinds of databases such as relational databases, transactional databases, etc. It has a great importance in data mining. Extracting relevant information from a huge collection of data by exploitation of data is called data mining. There is an increasing need of data mining by business people to extract valid and useful information from large datasets. Thus, data mining has its importance to discover hidden patterns from huge data stored in databases as well as data warehouse. Apriori algorithm has been one of the key algorithms in association rule mining. Classical Apriori algorithm is inefficient as it takes considerable amount of time to generate the desired output for mining the frequent itemsets owing to multiple scans on the database. In this research paper, a method has been proposed to improve the efficiency of Apriori algorithm by reducing the size of the database as well as reducing the time complexity for scanning the transactions.

Itemsets, Apriori algorithm, Association rule mining, Minimum support

[1] J. Han, M. Kamber, “Conception and Technology of Data Mining”, China Machine Press, China, 2007.
[2] U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, “From data mining to knowledge discovery in databases”, Vol 17, Issue 3, AI magazine, pp. 37-54, 1996.
[3] S. Rao, R. Gupta, “Implementing Improved Algorithm over APRIORI Data Mining Association Rule Algorithm”, International Journal of Computer Science And Technology,Vol 3, Issue 1, pp. 489-493, 2012.
[4] H. H. O. Nasereddin,“Stream data mining”, International Journal of Web Applications, Vol 1, Issue 4, pp. 183–190, 2009.
[5] M. Halkidi, “Quality assessment and uncertainty handling in data mining process”, In Proceedings of EDBT Ph.D. Workshop, Germany, 2000.
[6] R. Agarwal, R. Srikant, “Fast Algorithm for mining association rules”, In Proceedings of 20th VLDB Conference, pp 487-499, 1994.
[7] Sakshi Aggarwal, Ritu Sindhu, “An Approach of Improvisation in Efficiency of Apriori Algorithm”, In Proceedings of International Journal of Computer and Communication System Engineering, Vol 2, Issue 5, pp. 659-664, 2015.
[8] S. Kumar , S. Karanth , A. Prabhu, and B. Kumar , “Improved Apriori Algorithm Based On Bottom Upapproach Using Probability And Matrix”,IJCSI,2012.
[9] F. H. AL-Zawaidah, Y. H. Jbara, A. L. Marwan, “An Improved Algorithm for Mining Association rules in Large Databases”,World of Computer Science and Information Technology, Vol 1, Issue 7, pp. 311-316, 2011.
[10] X. Wu, V. Kumar, J. Ross Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, P. S. Yu, Z.-H. Zhou, M. Steinbach, D. J. Hand, D. Steinberg, “Top 10 algorithms in data mining,”, Knowledge and Information Systems, Vol 14, Issue 1, pp. 1–37, 2007.
[11] F. Crespo, R. Weber, “A methodology for dynamic data mining based on fuzzy clustering”, Fuzzy Sets and Systems, Vol 150, Issue 2, pp. 267–284, 2005.
[12] R. Agrawal, T. Imielinski, A. Swami, “Mining association rules between sets of items in large database,”, In Proceedings of ACM SIGMOD International Conference on Management of Data, Vol 22, Issue 2, pp. 207–216, 1993.
[13] R. Bhaskar, S. Laxman, A. Smith, A. Thakurta, “ Discovering frequent patterns in sensitive data”, In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, , pp. 503-512, 2010.
[14] B. Vo, M. Chi, H. C. Minh, “Fast Algorithm for Mining Generalized Association Rules”, International Journal of Database Theory and Application, Vol 2, Issue 12, pp. 161–180, 1994.