A Novel Approach for Association Rule Mining using Pattern Generation

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Deepa S. Deshpande 1,*

1. MGM’s Jawaharlal Nehru Engineering College, Aurangabad, 431003, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2014.11.09

Received: 6 Feb. 2014 / Revised: 11 May 2014 / Accepted: 3 Jul. 2014 / Published: 8 Oct. 2014

Index Terms

Data Mining, Association Rule Mining, Frequent Item Set


Data mining has become a process of significant interest in recent years due to explosive rate of the accumulation of data. It is used to discover potentially valuable implicit knowledge from the large transactional databases. Association rule mining is one of the well known techniques of data mining. It typically aims at discovering associations between attributes in the large databases. The first and the most influential traditional algorithm for association rule discovery is Apriori. Multiple scans of database, generation of large number of candidates item set and discovery of interesting rules are the main challenging issues for the improvement of Apriori algorithm. Therefore in order to decrease the multiple scanning of database, a new method of association rule mining using pattern generation is proposed in this paper. This method involves three steps. First, patterns are generated using items from the transaction database. Second, frequent item set is obtained using these patterns. Finally association rules are derived. The performance of this method is evaluated with the traditional Apriori algorithm. It shows that behavior of the proposed method is much more similar to Apriori algorithm with less memory space and reduction in multiple times scanning of database. Thus it is more efficient than the traditional Apriori algorithm.

Cite This Paper

Deepa S. Deshpande, "A Novel Approach for Association Rule Mining using Pattern Generation", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.11, pp.59-65, 2014. DOI:10.5815/ijitcs.2014.11.09


[1]A. Amir, R. Feldman, and R. Kashi,”A new and versatile method for association generation”, Information Systems, Vol. 22, no. 6, pp.333-347, 1999

[2]C. C. Aggarwal and P.S.Yu, ”Online generation of association rules”, Proc.14th Int’l Conf. Data Engg. pp 402-411, 1998.

[3]C. Hidber ,”Online Association Rule Mining”, proc ACM SIGMOD Conf. pp 145-154,1999.

[4]F. Coenen, G. Goulbourne and P.H. Leng, “Computing Association Rules Using Partial Totals”, proc. 5th European Conf. Principles and Practice of Knowledge Discovery in Databases ,pp.54-66,2001 

[5]J. Han, M. Kamber (2001), Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, CA,SIGMOD Conf.,pp-1-12,2000

[6]J. Han, J. Pei, and Y. Yin,”Mining frequent patterns without candidate generation”, Proc. ACM SIGMOD International Conference on Management of Data, May 16-18, Dallas, Texas, pp 1-12

[7]J. S. Park, M.S. Chen P.S. Yu,” Using Hash-based Method with Transaction Trimming for Mining Association Rules”, IEEE Trans. Knowledge and Data Engg., vol 9,no. 5, pp. 813-824, Sept/Oct 1997.

[8]K. Wang, Y. H, J. Han, “Mining frequent item sets using support constraints”, in Proceedings of the 26th International Conference on Very Large Data Bases, 2000, pp. 43-52 

[9]Lin T.Y.,” Data Mining and Machine Oriented Modeling: A Granular Computing Approach”, Journal of Applied intelligence, Oct 2000. 

[10]Lin T.Y., Lounie E,”Finding Association Rules using Fast bit Computation: Machine-Oriented Modeling”,IS-MIS-2000.

[11]M. Houtsma and A. Swami,” Set oriented mining of association rules”, Research report RJ 9567, IBM Almaden Research Center, San Jose, California, Oct 1993. 

[12]M. J. Zaki, ”Scalable algorithms for association mining”, IEEE Trans. Knowledge Data Eng. ,vol. 12,no. 3,pp 372-390 May/June 2000.

[13]M. J. Zaki et al., “New Algorithms for fast discovery of Association Rules”, in KDD-97

[14]M. J. Zaki ,” Generating non-redundant association rules”, in KDD-2002 

[15]Morzy T., Zakrzewiez M.,” Group Bitmap Index: A Structure for Association Rule Retrieval”, Prod. of the 4th International Conf. on Knowledge Discovery and Data Mining (KDD-98). 

[16]Ni Tian-quan, Wang Jain-dong, Peng Xiao-bing and Liu Yian, ”A fast association rules mining algorithm for dynamic updated databases”, Inform. Tech. J. 8 (8); 1235-1241, 2009, ISSN 1812-5638.

[17]P. Shenoy, J.R. Haritsa, S. Sudarshan, G.Bhalotia, M. Bawa and D. Shah ,“Turbo-Charging Vertical Mining of Large Databases”, Proc. ACM SIGMOD Conf.,pp.22-33,2000

[18]R. Agrawal and R. Srikant, “Fast algorithms for mining association rules”, Proc. 20th Int’l. Conf. Very Large Databases, pp.487-499,1994

[19]R. Agrawal, R. Srikant, “Mining sequential patterns”, The Eleventh IEEE International Conference on Data Engineering, 1995, pp. 3-14

[20]R. Agrawal, R. Srikant, Q, Vu, “Mining association rules with item constraints”, The Third International Conference on Knowledge Discovery in Databases and Data Mining, 1997, pp.67-73 

[21]R. Agrawal, T. Imielinski, A. Swami, “Database mining: a performance perspective”, IEEE Transactions on Knowledge and Data Engineering.5 (6) (1993) 914-925.

[22]R. Agrawal, T. Imielinski, A. Swami, “Mining association rules between sets of items in large databases”, In Proc of the ACM SIGMOD International Conference on Management of Data, Jun 1993,Washington, D.C.,USA,pp.207-216.

[23]S. Brin, R. Motwani, J.D. Ullman and S. Tsur, “ Dynamic itemset counting and implication rules for market basket data”, In Proc. of ACM SIGMOD Int’l conference on Management of Data ,1997,26(2),255-264

[24]Savasere A.,E. Omiecinski and S. Navathe,” An efficient algorithm for mining association rules in large database”, proc. of 21st international Conf. on Very Large Database,(ICLD 1995), Zurich, Swizerland,pp. 432-443.

[25]Yew-Kwong Woon, Wee-Keong Ng, and Ee-Peng Lim,”A support ordered trie for fast frequent item set discovery”, IEEE Trans. On Knowledge And Data Engineering”, Vol.16, No.7, July 2004

[26]Predrag Stansic and Savo Tomovic,” Mining association rules from transaction databases and Apriori multiple algorithm”,ISBN : 978-972-8924-68-3 @ 2008 IADIS

[27]Hinbing Liu and Baisheng Wang,” An association rule mining algorithm based on Boolean matrix”, Data Science Journal, Vol. 6, Supplement, 9, Sept 2007

[28]Ulrich Guntzer, Jochen Hipp and Gholamreza Nakhaeizadeh, “Algorithms for Association Rule Mining- A General Survey and Comparison”, ACM SIGKDD, Vol.2,Issue 1,July 2000,pp.58-64

[29]Thabet Slimani and Amor Lazzez,” Efficient Analysis of Pattern and Association Rule Mining Approaches”, International Journal of Information Technology and Computer Science,2014,03,70-81

[30]Liu X., Zhai K., & Pedrycz W. “An improved association rules mining method”, Expert Systems with Applications, 2012 39(1):1362–1374. doi:10.1016/j. eswa.2011.08.018 

[31]Gouda, K. and Zaki,M.J. GenMax, “An Efficient Algorithm for Mining Maximal Frequent Itemsets”, Data Mining and Knowledge Discovery, 2005, 11: 1-20. 

[32]Grahne G. and Zhu G, “Fast Algorithms for frequent itemset mining using FP-trees”, in IEEE transactions on knowledge and Data engineering, 2005,17(10):1347-1362. 

[33]Kamrul Shah, Mohammad Khandakar, Hasnain Abu. “Reverse Apriori Algorithm for Frequent Pattern Mining”, Asian Journal of Information Technology, 2008, :524-530, ISSN: 1682-3915. 

[34]Praksh S., Parvathi R.M.S, “An enhanced Scalling Apriori for Association Rule Mining Efficiency”, European Journal of Scientific Research, 2010, 39:257-264, ISSN: 1450-216X. 

[35]Rao, S., Gupta, P. “Implementing Improved Algorithm Over Apriori Data Mining Association Rule Algorithm”, IJCST.2012, 3 (1), 489-493.