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Advanced Neuro-Fuzzy Approach for Social Media Mining Methods using Cloud

by Rajeev Kumar, M.K. Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 10
Year of Publication: 2016
Authors: Rajeev Kumar, M.K. Sharma
10.5120/ijca2016908927

Rajeev Kumar, M.K. Sharma . Advanced Neuro-Fuzzy Approach for Social Media Mining Methods using Cloud. International Journal of Computer Applications. 137, 10 ( March 2016), 56-58. DOI=10.5120/ijca2016908927

@article{ 10.5120/ijca2016908927,
author = { Rajeev Kumar, M.K. Sharma },
title = { Advanced Neuro-Fuzzy Approach for Social Media Mining Methods using Cloud },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 10 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 56-58 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number10/24315-2016908927/ },
doi = { 10.5120/ijca2016908927 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:38:03.782825+05:30
%A Rajeev Kumar
%A M.K. Sharma
%T Advanced Neuro-Fuzzy Approach for Social Media Mining Methods using Cloud
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 10
%P 56-58
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social media sites are now very popular medium for showing your views and opinions to others with a great amount of various types of information uploaded by the social media users, a social web page can be a collection of pages, audio files, photographs, images, video files and other forms of data in structured or unstructured form. It is also huge, diverse, and dynamic, hence raises the scalability. The primary aim of web mining is to extract useful information and knowledge from web. Web mining is a part of data mining which relates to various research communities such as information retrieval, database management systems and Artificial intelligence. A hybrid Neuro-fuzzy methods may faster the process of web mining on social media sites , this paper attempts to analyze benefits and drawbacks of ANN and Fuzzy approaches for mining the social media data sets .

References
  1. O.A. Mohamed Jafarand R. Sivakumar, “Ant-based Clustering Algorithms: A Brief Survey”, International Journal of Computer Theory and Engineering, Vol. 2, No. 5, October, 2010 1793-8201.
  2. Richa Gupta, “Web Mining using Artificial Ant Colonies: A Survey”, International Journal of Computer Trends and Technology (IJCTT) – volume 10 number 1 – Apr 2014.
  3. J. Deneubourg -L., S. Goss, N. Franks, A. Sendova-Franks, C. Detrain, L. Chrétien, The dynamics of collective sorting: robot-like ants and ant-like robots. Proceeding of the first international conference on simulation of adaptive behavior, pp. 356–365, MIT Press, 2011.
  4. J. Handl, B. Meyer, Ant-based and Swarm-based clustering, Swarm Intelligence, 1, pp. 95–113, 2007.
  5. E. Lumer, B. Faieta, Diversity and adaptation in populations of clustering ants. Proceeding of the third international conference on simulation of adaptive behavior, pp. 501–508, MIT Press, 2014.
  6. E.H. Chi, A. Rosien, and J. Heer, “LumberJack: Intelligent Discovery and Analysis of Web User Traffic Composition”, In Proceedings of ACMSIGKDD Workshop on Web Mining for Usage Patterns and User Profiles, Canada, ACM Press, 2012.
  7. R. Cooley, “Web Usage Mining: Discovery and Application of Interesting patterns from Web Data”, Ph. D. Thesis, University of Minnesota, Department of Computer Science, 2010.
  8. J. Heer, and E.H. Chi, “Identification of Web User Traffic Composition using Multi- Modal Clustering and Information Scent”, In Proc. of the Workshop on Web Mining, SIAM Conference on Data Mining, pp. 51-58, 2001.
  9. S.E. Jespersen, J. Thorhauge, and T. Bach, “A Hybrid Approach to Web Usage Mining, Data Warehousing and Knowledge Discovery”, in Y. Kambayashi, W. Winiwarter, M. Arikawa , eds., LNCS 2454, pp. 73-82, 2002.
  10. L. A. Zadeh, “Fuzzy logic, neural networks, and soft computing,” Commun. AGM, vol. 37, pp. 77–84, 1994.
  11. S. K. Pal, A. Ghosh, and M. K. Kundu, Eds., Soft Computing for Image Processing. Heidelberg, Germany: Physica-Verlag, 2000.
  12. S. K. Pal and S. Mitra, Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing. New York: Wiley, 1999.
  13. S. Mitra, S. K. Pal, and P. Mitra, “Data mining in soft computing framework: A survey,” IEEE Trans. Neural Networks, vol. 13, pp. 3–14, Jan. 2001.
  14. Kumar Rajeev, M.K. Sharma, “Collision of ICT for Cloud Computing in e- Governance”. New York Science Journal; 6(5), pp78-80, 2013
  15. Kumar Rajeev, M.K. Sharma, “Role of Energy aware routing protocols in e-Governance”. New York Science Journal; 6(5), pp 81-83, 2013
  16. Rajeev Kumar, Dr. M. K. Sharma, “Cloud Application of e-Governance System Using Advanced Wireless Networks”. International Journal of Researcher 2013; 5(6):26-29. (ISSN: 1553-9865).
Index Terms

Computer Science
Information Sciences

Keywords

Soft computing social media ANN ACO GA Fuzzy set Cloud Application

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