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Improving Efficiency of Apriori Algorithm using Cache Database
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 75 - Number 13 |
| Year of Publication: 2013 |
| Authors: Priyanka Asthana, Diwakar Singh |
10.5120/13171-0781
|
Priyanka Asthana, Diwakar Singh . Improving Efficiency of Apriori Algorithm using Cache Database. International Journal of Computer Applications. 75, 13 ( August 2013), 15-20. DOI=10.5120/13171-0781
Abstract
One of the most popular data mining approach to find frequent itemset in a given transactional dataset is Association rule mining. The important task of Association rule mining is to mine association rules using minimum support value which is specified by the user or can be generated by system itself. In order to calculate minimum support value, every time the complete database has to be scanned for each item in the transaction. This decreases the time complexity of the algorithm. Here we proposed a new algorithm which scan the database once and create a cache database for each transaction using hash map. This cache copy is then used to search for frequent item sets. Due to which the overhead of scaning complete database for each item is reduced, and efficiency is increased.
References
- S. Sangeetha, "Verdict of Association Rule Using Systematic Approach of Time Slicing for Efficient Pattern Discovery " Proceeding of 2012 International Conference on Computing, Electronics and Electrical Technologies [ICCEET].
- K. Vanitha and R. Santhi, "Using Hash Based Apriori Algorithm to Reduce the Candidate 2- itemsets for Mining Association Rule "Proceeding of H. S. Behera et al, Journal of Global Research in Computer Science, Volume 2, No 5, 2011.
- B. Liu, W. Hsu, Y. Ma, Mining association rules with multiple minimum supports, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), San Diego, CA, USA, 1999.
- M. C. Tseng, W. Y. Lin, "Mining generalized association rules with multiple minimum supports", International Conference on Data Warehousing and Knowledge Discovery (DaWaK'01), Munich, Germany, 2001, pp. 11 – 20.
- H. Mannila, "Database methods for data mining", Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD '98) tutorial, New York, NY, USA, 1998.
- W. Lee, S. J. Stolfo, K. W. Mok, "Mining audit data to build intrusion detection models", Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD '98), New York, NY, USA, 1998.
- J. Han, Y. Fu, "Discovery of multiple-level association rules from large databases", Proceedings of the 21th Very Large DataBases Conference (VLDB'95), Zurich, Switzerland, 1995, pp. 420– 431
- Ya -Han Hu and Yen-Liang Chen, "Mining Association rules with multiple minimum Supports: a new mining algorithm and a Support tuning mechanism", Elsevier B. V. All rights reserved, Decision Support Systems, 42, (2006) pp. 1-24.
- http://www. arl. wustl. edu/projects/fpx/cse535/lecture/cse535_lecture6_Hash_Functions. pdf
- http://en. wikipedia. org/wiki/Apriori_algorithm.
- XindongWu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg, "Top 10 algorithms in data mining", © Springer-Verlag London Limited 2007.
- R. Agrawal. T. Imielinski. and A Swami, "Mining Association Rules between Sets of Items in Large Databases", Proc. 1993 ACM SIGMOD Int'I Conf. Management of Data ( SIGMOD '93), pp. 207-216, 1993.
- R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," Proc. 20th Int'I Conf. Very Large Data Bases, pp. 487-499, 1994.
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