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Reseach Article

Efficient Algorithm and Framework for Interesting Patterns Generation for optimization of Association rule mining using genetic Algorithm for Social Networking Sites

by Karuna Nidhi Pandagre, S. Veenadhari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 11
Year of Publication: 2018
Authors: Karuna Nidhi Pandagre, S. Veenadhari
10.5120/ijca2018917692

Karuna Nidhi Pandagre, S. Veenadhari . Efficient Algorithm and Framework for Interesting Patterns Generation for optimization of Association rule mining using genetic Algorithm for Social Networking Sites. International Journal of Computer Applications. 181, 11 ( Aug 2018), 21-29. DOI=10.5120/ijca2018917692

@article{ 10.5120/ijca2018917692,
author = { Karuna Nidhi Pandagre, S. Veenadhari },
title = { Efficient Algorithm and Framework for Interesting Patterns Generation for optimization of Association rule mining using genetic Algorithm for Social Networking Sites },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2018 },
volume = { 181 },
number = { 11 },
month = { Aug },
year = { 2018 },
issn = { 0975-8887 },
pages = { 21-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number11/29817-2018917692/ },
doi = { 10.5120/ijca2018917692 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:05:43.040117+05:30
%A Karuna Nidhi Pandagre
%A S. Veenadhari
%T Efficient Algorithm and Framework for Interesting Patterns Generation for optimization of Association rule mining using genetic Algorithm for Social Networking Sites
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 11
%P 21-29
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social Networking (SN) plays an important part in real life because people is already connected in any types of social network like facebook, twitter, linkedin, instagram etc. SN gives a platform to people to share their ideas to each other. Moreover this type of online community, people may share their common interest. SN is a medium of making link with people having unique or common interest and goals. But there are various challenges in SN services. The major challenge is privacy it means keeping your information private isn’t just about your own choices. It’s about your friends’ choices, too. In this article a novel Algorithm proposed for Interesting Patterns Generation (AIPG) and framework for interesting patterns generation from weblog. This approach is also helpful in SN

References
  1. Chu H., Deng D. and Park J. 2011. Live Data Mining Concerning Social Networking Forensics Based on a Facebook Session Through Aggregation of Social Data, IEEE Journal On Selected Areas In Communications, Vol. 29, No. 7.
  2. Bakariya B., Thakur G. 2014. User Identification Framework in Social Network Services Environment, Informatica Economică vol. 18, no. 2, pp 15-23.
  3. Yuan M., Chen L., Yu P. and Yu T. 2013. Protecting Sensitive Labels in Social Network Data Anonymization, IEEE Transactions on Knowledge and Data Engineering, VOL. 25, NO. 3.
  4. Hajian B. and White T. 2011. Modelling In-fluence in a Social Network: Metrics and Evaluation, IEEE International Confer-ence on Privacy, Security, Risk, and Trust, IEEE International Conference on Social Computing, 2011.
  5. G. Thakur, Bakariya B. and Mohbey K. 2012. An Inclusive Survey on Data Preproc-essing Method Used in Web Usage Min-ing, presented in Seventh International Conference on Bio-Inspired Computing: Theories and Application, (BIC-TA 2012), ABV-Indian Institute of Informa-tion Technology and Management Gwalior, December 14 - 16.
  6. Zhang K., Du H., Feldman M. 2017. Maximizing influence in a social network: Improved results using a genetic algorithm, Physica A,pp. 20–30.
  7. Chang S., Liu A. and Shen W. 2017. User trust in social networking services: A comparison of Facebook and LinkedIn, Computers in Human Behavior.
  8. Jang B. and Yoon J. 2018. Characteristics Analysis Of Data From News And Social Network Services, IEEE Access.
  9. Ha T. and Hoang D. 2017. An assistive healthcare platform for both social and service networking for engaging elderly people, 23rd Asia-Pacific Conference on Communications (APCC).
  10. Rathorea S., Sangaiahb A. and Parka J. 2017. A novel framework for internet of knowledge protection in social networking services, Journal of Computational Science, pp.55–65.
  11. Bahri L., Carminati B. and Ferrari E. 2018. Decentralized privacy preserving services for Online Social Networks, Online Social Networks and Media, Vol.6, pp. 18–25.
  12. Man K., Tang K., and Kwong S. 1996. Genetic Algorithms: Concepts and Applications, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 43, NO. 5.
  13. Bakariya B., Thakur G. 2017. Pattern Mining Approach for Social Network Service, National Academy Science Letters, Springer, Volume 40, Issue 3, pp 183–187, June 2017.
  14. Bakariya B., Thakur G. 2015. An Efficient Algorithm for Extracting High Utility Itemsets from Web Log Data,The Institution of Electronics and Telecommunication Engineers (IETE) Technical Review, Volume-32, Issue-02, March- 2015, Pages 151-160.
  15. Bakariya B., Chaturvedi K., Singh KP and Thakur GS. 2016. Efficient Approach for Mining Top-k Strong Patterns in Social Network Service, 5th International Conference on Eco-friendly Computing and Communication Systems (ICECCS-2016) Dec. 8-9, IEEE, MANIT Bhopal.
  16. Chaturvedi K., Patel R. and DK. 2015. Fuzzy C-Means based Inference Mechanism for Association Rule Mining: A Clinical Data Mining Approach,INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, Volume 6, Issue 6, Pages 103-110, 2015.
  17. Bakariya B. and Thakur G.S. 2016. Mining Rare Itemsets from Weblog Data, National Academy Science Letters, Springer, Volume-39, Issue-05, pp 359–363.
  18. Hassani M., TöwsAlfredo D. and Seidl C. 2017. BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams, International Journal of Data Science and Analytics, pp 1–17.
  19. Birla B., Patel S. and Sunhare H. 2013. Comprehensive Framework for Pattern Analysis through Web Logs Using Web Mining: A Review , International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 2, Issue. 4, , pg.32–37.
  20. Thilagu M. and Nadarajan R. 2012. Efficiently Mining of Effective Web Traversal Patterns with Average Utility,Procedia Technology, Volume 6, Pages 444-451.
  21. Kuramochi M. and Karypis G. 2004. An efficient algorithm for discovering frequent subgraphs, IEEE Transactions on Knowledge and Data Engineering, Volume: 16 Issue: 9.
  22. Bin W. and Zhijing L. 2003. Web mining research,Computational Intelligence and Multimedia Applications, 2003. ICCIMA.
  23. Singh A., Sharma D. and Pathak A. 2013, Web Usage Mining: A Concise Survey on Tools and Applications. International Journal of Computer Applications, Vol. 74, Issue 1, pp.1-7.
  24. Pandagre, Ms Karuna Nidhi, and S. Veenadhari. Efficient Approach for Finding Strong Patterns from Weblog using Web Usage Mining. International Journal of Engineering Science Invention ,Vol 7,Issue 1,pp.06-14
Index Terms

Computer Science
Information Sciences

Keywords

Social Network Private Data Weblog Visitors