Extraction of Association Rules for Customer's Shopping on E-commerce Mobile Applications
DOI:
https://doi.org/10.59992/Keywords:
Market Basket Analysis, Association Rules, Customer's Shopping, E-commerceAbstract
This paper presents an approach to connecting requirements engineering activities into a process mobile application for association mining of shopping based on the Apriori algorithm. The proposed system shows and displays some offers and deals from various branches. The system provides the analytics for the seller as the demand for some varieties and the weakness in other varieties and the whole application is organized on the Cloud. The architecture includes three levels; the front-end, middle, and back-end. The front-end level of the site-based Mobile shopping application is made up of Android Mobile devices, to buy miscellaneous products from various nearby branches. The front-end level also displays the link between items purchased. The middle repository level provides a Web service to generate returns from a relational database.
The Exchanged information and data between application and servers is stored in the Cloud. The background level offers a Web server and a MySQL database. In this paper, we propose an architecture that reduces the communication overhead in existing Mobile Agent-based Distributed Association Rule Mining (MAD-ARM).
References
[1] Emil Almrot, Sebastian Andersson. A study of the advantages & disadvantages of mobile cloud computing versus native environment. Bachelor Thesis in Software Engineering May 2013.
[2] Raubal, M. and Rinner, C., 2004. Multi-Criteria Decision Analysis for Location Based Services. In S.A. Brandt (ed.) Proceedings of the 12th International Conference on Geoinformatics, pp. 47-53.
[3] A. Saleem Raja, E. George Dharma Prakash Raj, “MAD-ARM: Mobile Agent based Distributed Association Rule Mining”, International Conference on Computer Communication and Informatics (ICCCI -2013), Jan. 09 – 11, 2013, Coimbatore, INDIA, DOI 10.1109/ICCCI.2013.6466112.
[4] Agrawal R., Imielinski T., and Swami A. 1993. Mining association rules between sets of items in very large databases. Proceedings of the A CM SIGMOD Conference on Management of Data, pages 207-216, Washington D. C.
[5] Agrawal R., and Srikant R. 1994. Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, pages 478-499.
[6] Savasere A., Omiecinski E., and Navathe S. 1995. An Efficient Algorithm for Mining Association Rules in Large Data Bases. Proceedings of the 21st International Conference on Very Large Data Bases. Zurich, Switzerland, pages 432-444.
[7] Hun J. and Fu Y. 1995. Discovery of Multiple-Level Association Rules from Large Databases. Proceedings of the 21st International Conference on Very Large Data Bases. Zurich, Switzerland, pages 420-431.
[8] Agrawal, R. and Srikant, R. Mining Generalized Association Rules. Proceedings of the 21st VLDB Conference. 1995
[9] Agrawal R., Imielinski T., and Swami A. 1993. Mining association rules between sets of items in very large databases. Proceedings of the A CM SIGMOD Conference on Management of Data, pages 207-216, Washington D. C.
[10] Srikant R., and Agrawal R. Mining quantitative association rules in large relational tables. Proceedings of the 1996 ACM SIGMOD Conference on Management of Data. Montreal, Canada.
[11] Shivani Rajput*, Rajeshree Pagar, Laxman Erande, Yogesh Alai. One Click Android Application for Shopping Based on Cloud. Proceedings of the 2015 IJSRSET | Volume 1 | Issue 5 | Print ISSN: 2395-1990 | Online ISSN: 2394-4099.
[12] Ozgur Cakir, Murat Efe Aras. A recommended engine by using association rules Procedia - Social and Behavioral Sciences 62 (2012) 452 – 456.
[13] Charu C. Aggarwal, Zheng Sun, Philip S. Yu. Online generation of profiles association rules - T.J. Watson Research Center Yorktown Heights, NY 10598 [email protected], Duke University, Durham, NC-27706 [email protected], T.J. Watson Research Center Yorktown Heights, NY 10598 [email protected].
[14] Manpreet Kaur, Shivani Kang. Market Basket Analysis: Identify the changing trends of market data using association rule mining, Procedia Computer Science 85 (2016) 78 – 85.
[15] S M Shamim, Angona Sarker, Ali Newaz Bahar, Md. Atiqur Rahman. A Review on Mobile Cloud Computing. International Journal of Computer Applications (0975 – 8887), Volume 113 – No. 16, March 2015.
[16] Standish Group, Extreme CHAOS, Found at www.standishgroup.com, 2001.
[17] J. Golbeck. Computing with social trust. Springer, 2009.
[18] S. Lim and A. Finkelstein. Stakerare: Using social networks and collaborative filtering for large-scale requirements elicitation, in press. IEEE Trans. on Softw. Eng., pages 1–32, 2011.
[19] S. Lim, D. Quercia, and A. Finkelstein. Stakenet: Using social networks to analyze the stakeholders of large-scale software projects. pages 295–304, Cape Town, South Africa, 2010. ACM/IEEE.
[20] B. Mobasher and J. Cleland-Huang. Recommender systems in requirements engineering. AI Magazine, 32(3):81–89, 2011.
[21] M. Pazzani and D. Billsus. Learning and revising user profiles: The identification of interesting web sites. Machine Learning, 27:313–331, 1997.
[22] S. Marczak, I. Kwan, and D. Damian. Social networks in the study of collaboration in global software teams. Munich, Germany, 2007.
[23] J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl. Grouplens: applying collaborative filtering to usenet news full text. Comm. of the ACM, 40(3):77–87, 1997.
[24] J. IyerandD. Richards. Evaluation framework for tools that manage requirements inconsistency. 2004.
[25] E. Tsang. Foundations of Constraint Satisfaction. Academic Press, London, 1993.
[26] R. Reiter. A theory of diagnosis from first principles. AI Journal, 23(1):57– 95, 1987.
[27] G. Ruhe, A. Eberlein, and D. Pfahl. Trade-off analysis for requirements selection. Journal of Software Engineering and Knowledge Engineering (IJSEKE), 13(4):354–366, 2003.
[28] A. Jameson, S. Baldes, and T. Kleinbauer. Two methods for enhancing mutual awareness in a group recommender system. In ACM Intl. Working Conference on Advanced Visual Interfaces, pages 48–54, Gallipoli, Italy, 2004.
[29] G. Ruhe and M. Saliu. The art and science of software release planning. IEEE Software, 22 (6):47–53, 2005.
[30] A. Schrijver. Theory of Linear and Integer Programming. John Wiley and Sons, 1998.
[31] D. McFadden. Rationality for economists. Jrnl. of Risk & Uncertainty, 19(1):73–105, 1999.
[32] S. Lichtenstein and P. Slovic. The Construction of Preference. Cambridge Univ. Press, 2006.
[33] I. Witten and E. Frank. Data Mining. Elsevier, 2005.
[34] Hans-J¨org Happel and Walid Maalej. Potentials and challenges of recommended systems for software development. In RSSE ’08: Proceedings of the 2008 international workshop on Recommended systems for software engineering. ACM, Nov 2008.
[35] A. Felfernig, G. Ninaus, H. Grabner, F. Reinfrank, L. Weninger, D. Pagano, and W. Maalej. An Overview of Recommender Systems in Requirements Engineering. Springer, pp. 315–332, 2013.
