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

Frequent Pattern Mining Method to Influence Social Network Users for Marketing in an Efficient Way

Author : Yashaswini P R ¹ Sanjay Kumar N V ²

Date of Publication :7th June 2016

Abstract: Social networks have become an important marketing tools for business to build brand pages to prompt their new products. If this medium is explored intelligently then the social network medium has a potential to provide many new ways to market the audience with the help of registered users indirectly, without knowing them. Social Network (Face book) has provided many tools for marketing purpose like groups, events, social ads. Fans’ user-experience diffusion results in great marketing power that people never seen. Typically, some of the fans in group are influence users. They are market movers which mean they can influence others buying decisions. Businesses can affect online influence users by giving them extra benefits to turn them into spokesmen. When analyzing the users’ scope of social networking, it can be concluded that the modern social communities influence in individual’s private life. However, who is the influential user? What period of time is appropriate for information to spread? In this study, a framework based on frequent pattern mining is proposed to find the influence users as well as the proper time to spread information. It also presents a survey based research from users and organizations for finding their views on the tagged based marketing on the social networking website The one day 24-hour period can be divided into successive time segments. An influence transaction that contains fans’ influence power will be defined in each time segment. After transactions being collected several days, helps in understanding the reason what are the barriers and what steps could be taken to make it effective for organizations, the frequent patterns can be found to deduce the proper time for influence users to spread information. The theoretical experiment is given to show how the proposed framework works.

Reference :

1. N. Agarwal, H. Liu, L. Tang, and P.S. Yu, (2008, February). “Identifying the influential bloggers in a community,” in Proc. of the Int. Conf. on Web Search and Data Mining, pp.207-218, February,2008.
2. S. Aral, L. Muchnik, and A. Sundararajan, “Distinguishing influencebased contagion from homophily-driven diusion in dynamic networks,” in Proc. of the National Academy of Sciences of the United States of America, vol. 106, no. 51, pp.21544- 21549, October, 2009
3. J. Brown and P. Reinegen, “Social ties and word-of mouth referral behavior,” Journal of Consumer Research, vol. 14, no. 3, pp.350-362, 1987.
4. K. Burke, “Network to drive revenue,” Target Marketing Magazine, vol. 29, no. 2, pp.25-26, February 2006.
5. P. Domingos and M. Richardson, “Mining the network value of customers,” in Proc. of the 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp.57- 66, August, 2001.
6. J. Goldenberg, B. Libai, E. Muller, “Talk of the network: a complex systems look at the underlying process of word-of-mouth,” Marketing Letters, vol. 12, no. 3, pp.211-223, 2001.
7. A. Goyal, F. Bonchi, and L.V. Lakshmanan, “Discovering leaders from community actions,” in Proc. of the 17th ACM Conf. on Information and Knowledge Management, pp.499-508, October, 2008.
8. G. Grahne, and J.F. Zhu, “Fast algorithms for frequent item set mining using FP-Trees,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 10, pp.1347-1362, October 2005.
9. P. Gundecha, and H. Liu, “Mining social media: A brief introduction,” in Proc. INFORMS TutORials in Operations Research, Phoenix, Arizona, USA, pp.1-17, October 14-17, 2012
10. J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” in Proc. of ACM SIGMOD Int. Conf. on Management of Data, Dallas, Texas, USA, pp.1-12, May 2000
11. T. Hu, S.Y. Sung, H. Xiong, and Q. Fu, “Discovery of maximum length frequent itemsets,” Information Sciences, vol. 178, no. 1, pp.69-87, January 2008.
12. H.-W. Hu, and S.-Y. Lee, “Study on influence diffusion in social network,” Int. Journal of Computer Science and Electronics Engineering, vol. 1, no. 2, pp.198- 204, 2013.
13. Y.-P. Huang, L.-J. Kao, and F.E. Sandnes, “Efficient mining of salinity and temperature association rules from ARGO data,” Expert Systems with Applications, vol. 35, no. 1-2, pp.59-68, Aug. 2008.
14. D. Kempe, J. Kleinberg, and É. Tardos, “Maximizing the spread of influence through a social network,” in Proc. of the 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp.37-146, August, 2003.
15. A. Porterfield, “5 new studies show Facebook a marketing powerhouse,” Social Media Examiner, March, 2010.
16. D. Qiu, H. Li, & Y. Li, “Identification of active valuable nodes in temporal online social network with attributes,” International Journal of Information Technology & Decision Making, vol. 13, no. 4, pp.839- 864, April 2014
17. M. Richardson and P. Domingos, “Mining knowledge-sharing sites for viral marketing,” in Proc. of he 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data mining, pp.61-70, August, 2002
18. M.F. Tsai, C.W. Tzeng, Z.L. Lin, & A.L. Chen, “Discovering leaders from social network by action cascade,” Social Network Analysis and Mining, vol. 4, no. 1, pp.1-10, January 2014.
19. X. Wu, V. Kumar, J. Ross Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. McLachlan, A. Ng, B. Liu, P. Yu, Z.- H Zhou, M. Steinbach, D. Hand, and D. Steinberg, “Top 10 algorithms in data mining,” Knowledge and Information Systems, vol. 14, no. 1, pp.1-37, January 2008.