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Shprior: A Customer Assistance System using Apriori Algorithm

IJCA Proceedings on International Conference on Recent Developments in Science, Technology, Humanities and Management
© 2018 by IJCA Journal
ICRDSTHM 2017 - Number 1
Year of Publication: 2018
Krishan Chawla
Mithilesh Joshi
Vedika Pathak
Ravi Tomar


Shoprior meaning priory recognizes what to shop. This would be a client help framework which would exhort client with respect to the buy he/she needs to make. Regularly we client are in difficulty of what to shop next. This product will recommend related things to be purchased. This depends on Apriori Algorithm. Apriori is a fundamental calculation. The name of the calculation depends on the way that it utilizes past learning of incessant thing set properties. Once the successive itemsets from exchanges in a database D have been found. It is direct to create solid affiliation rules from them. Bolster S and Confidence C will be entered by the client for showing the proper blends. Support S and Confidence C will be entered by the user for displaying the appropriate combinations.


  • Jiawei Han, Micheline Kamber, 2000. Data Mining –Concepts and Techniques, Elsevier.
  • Parvinder S. Sandhu Dalvinder, Dhaliwal S. Panda S. N. ,2011. Mining utility-oriented association rules: An efficient approach based on profit and quantity, International Journal of the Physical Science, Volume 6, Issue 2, pp. 301-307.
  • PilliaJyothi, 2011. User centric approach to itemset utility mining in Market Basket Analysis, International Journal on Computer Science and Engineering, Volume 3, pp. 393-400.
  • S. Elnaffar, W. Powley, D. Benoit, P. Martin, 2003. Today's DBMSs: How Autonomic are They?, Proceedings of the 14thDEXA Workshop, Prague, pp. 651-654.
  • D. Menasec, Barbara, R. Dodge,2001. Preserving QoS of E-Commerce Sites through Self-Tuning: A Performance Model Approach, Proceedings of 3rdACM-EC Conference, Florida, pp. 224-234.
  • D. G. Benoit, 2000. Automated Diagnosis and Control of DBMS Resources, EDBT Ph. D Workshop, Konstanz.
  • B. K. Debnath, 2007. SARD: A Statistical Approach for Ranking Database Tuning Parameters. https://www. dtc. umn. edu/disc/resources/debnath1. pdf
  • K. P. Brown, M. J. Carey, M. Livny,1996. Goal-Oriented Buffer Management Revisited, Proceedings of ACM SIGMOD Conference, Montreal, pp. 353-364.
  • P. Martin, H. Y. Li, M. Zheng, K. Romanufa, W. Poweley, 2002. Dynamic Reconfiguration Algorithm: Dynamically Tuning Multiple Buffer Pools, Proceedings of 11th DEXA conference, London, pp. 92-101.
  • P. Martin, W. Powely, H. Y. Li, K. Romanufa, 2002. Managing Database Server Performance to Meet QoS Requirements in Electronic Commerce System, International Journal of Digital Libraries, Volume 8, Issue 1, pp. 316-324.