Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Augmentation of Apriori Algorithm

Author : Nivedita 1 Jhelum Dasgupta 2 Razauddin 3

Date of Publication :7th April 2016

Abstract: There is hasty evolution of system and information technology, ceaseless information has gained people attention progressively. While there is need of information at a fast paced flow, the investigation and mining of the information along with the regulations deeply jigged among statistics are of prime importance. Data mining knowledge is to formulate and consider the information, which extracts and find awareness since the piles of statistics, therefore how to define the information mined. Apriori algorithm is in trend and a typical method in data mining. The basic proposal of assumed procedure is in the direction to have a helpful model in wide-range of statistics sets. The procedure lacks in numerous domains. This research deals through the apriori algorithm, and varied procedure is being projected to enhance the strength of apriori algorithm. The algorithm is basically used for association rule mining field.

Reference :

    1. R. Agrawal, and R. Srikant, “Fast Algorithms for Mining Association Rules”, In Proc. VLDB 1994, pp.487-499
    2. A. Savasere, E. Omiecinski, and S. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases”, In VLDB’95, pp.432-443, Zurich, Switzerland
    3. S. Brin, et a1. Dynamic item set counting and implication rules for market basket data. Proceedings of the ACM SIGMOD International Conference on Management of Data. 1997. 123-140
    4. J. S. Park, M. S. Chen, P. S. Yu. Efficient parallel data mining of association rules. 4th International Conference on Information and Knowledge Management, 1995, 11: 233- 235P
    5. Jeffrey Dean,Sanjay Ghemawat.Map/Reduce:Simplified Data Processing on Large Clusters[R]. OSDI’04: Sixth Symposium on Operating System Design and Implementation 2004
    6. Li N., Zeng L., He Q. & Shi Z. (2012). Parallel Implementation of Apriori Algorithm Based on MapReduce. In:Proceedings of the 13th ACM International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing (SNPD ‘12). Kyoto, IEEE: 236–241.
    7. Lin M., Lee P. & Hsueh S. (2012). Apriori-based Frequent Item set Mining Algorithms on MapReduce. Proc. of the 16th International Conference on Ubiquitous Information Management and Communication (ICUIMC ‘12). New York, NY, USA, ACM: Article No.76

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