Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

Mining Association Rules using Hash Table

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 57 - Number 8
Year of Publication: 2012
K. Rajeswari
V. Vaithiyanathan
Swati. Tonge
Rashmi Phalnikar

K Rajeswari, V Vaithiyanathan, Swati.tonge and Rashmi Phalnikar. Article: Mining Association Rules using Hash Table. International Journal of Computer Applications 57(8):7-11, November 2012. Full text available. BibTeX

	author = {K. Rajeswari and V. Vaithiyanathan and Swati.tonge and Rashmi Phalnikar},
	title = {Article: Mining Association Rules using Hash Table},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {57},
	number = {8},
	pages = {7-11},
	month = {November},
	note = {Full text available}


Data mining is a field which searches for interesting knowledge or information from existing massive collection of data. In particular, algorithms like Apriori help a researcher to understand the potential knowledge, deep inside the data base. But due to the large time consumed by Apriori to find the frequent item sets and generate rules, several applications cannot use this algorithm. In this paper, we describe the modification of Apriori algorism, which will reduce the time taken for execution to a larger extent.


  • Srikant, R and Agarwal, R. 1995. Mining Generalisation Association Rules. In Proceedings of 21st VLDB Conference. pp 407-419.
  • Agrawal, Srikant, R. 1994. Fast Algorithms for Mining Association Rules. Proc. of the 20th Int'l Conference on Very Large Databases, Santiago, Chile
  • Agarwal R, Skikant R. 1996. Mining Quantitative Rules in Large Relational Tables [C] / / Proc of the ACM SIGMOD Conf on Management of Data. 1996:1-12.
  • Han J,Kamber M. Data Mining:Concepts and Techniques. Higher Education Press,200I.
  • Jiawei Han, Micheline Kamber, Data Mining Concepts and Techniques, 2nd ed. China Machine Press, 2006, pp. 155–160.
  • Xie, Jianhua Wu, Qingquan Qian. 2009. Feature Selection Algorithm Based on Association Rules Mining Method. IEEE.
  • X. Luo and W. Wang, "Improved Algorithms Research for Association Rule Based on Matrix," 2010 International Conference on Intelligent Computing and Cognitive Informatics, pp. 415–419, Jun. 2010.
  • Libing Wu , KuiGong , Fuliang Guo "Research on Improving Apriori Algorithm Based on Interested Table",IEEE,2010
  • R. Chang and Z. Liu, "An Improved Apriori Algorithm," no. Iceoe, pp. 476–478, 2011.
  • CHARM: An Efficient Algorithm for Closed Itemset Mining by Mohammed J. Zaki and Ching-Jui Hsiao
  • N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In 7th Intl. Conf. on Database Theory, January 1999.
  • Pang-Ning Tan, Michael Steinbach, Vipin Kumar "Introduction to Data Mining", Addison Wesley.