CFP last date
20 May 2024
Reseach Article

A Study on Web usage Data Mining in Online Sales and SASF Crawler in Online Advertisement

by M. Anisha, P. Joyce Beryl Princess
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 129 - Number 15
Year of Publication: 2015
Authors: M. Anisha, P. Joyce Beryl Princess
10.5120/ijca2015907070

M. Anisha, P. Joyce Beryl Princess . A Study on Web usage Data Mining in Online Sales and SASF Crawler in Online Advertisement. International Journal of Computer Applications. 129, 15 ( November 2015), 13-16. DOI=10.5120/ijca2015907070

@article{ 10.5120/ijca2015907070,
author = { M. Anisha, P. Joyce Beryl Princess },
title = { A Study on Web usage Data Mining in Online Sales and SASF Crawler in Online Advertisement },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 129 },
number = { 15 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume129/number15/23148-2015907070/ },
doi = { 10.5120/ijca2015907070 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:23:29.742423+05:30
%A M. Anisha
%A P. Joyce Beryl Princess
%T A Study on Web usage Data Mining in Online Sales and SASF Crawler in Online Advertisement
%J International Journal of Computer Applications
%@ 0975-8887
%V 129
%N 15
%P 13-16
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the world of Internet, users always expects the services, provided by the service providers must be easy to access. When online users need to retrieve information while doing online sales, then the ease of use depends on the frequency of the items that are available in the product recommendations. To achieve this, the crawlers are used to retrieve and download the required information from the web pages. To improve the performance of product recommendations Self Organizing Maps are used. A study has been made about Crawlers and Self Organizing Map.

References
  1. M.Balabanovic (1997), “An Adaptive Web Page Recommendation Service”, Proceedings of the 1st International Conference on Autonomous Agents, pp:378-385.
  2. C. Basu,H.Hirsh,V.Cohen (1998),“ Recommendation as classification: using social and content-based information in recommendation”, Proceedings of the 15th National Conference on Artificial Intelligence,pp:714-720.
  3. E.Frias-Martinez, G. Magoulas,S.Chen, R. Macredie (2006), “Automated User Learning for Text Categorization”, Proceedings of the International Journal of Information Management, pp: 19-25.
  4. Hai Dong, Member, IEEE, and Farookh Khadeer Hussain (2014),” Self-Adaptive Semantic Focused Crawler for Mining Services Information Discovery”, IEEE Transactions on Industrial Informatics, vol. 10, no.2.
  5. Jaytrilok Choudhary, Devshri Roy,( 2013),“Priority based Semantic Web Crawler “, International Journal of Computer Applications ,Vol. 81, No 15.
  6. T.Kohonen (1981), “Construction of similarity diagrams for phonemes by a Self Organizing Algorithm”, Technical Report TKK- FA463,Helsiniki University of Technology,Espoo,Finland.
  7. T.Kohonen (1982), “ Self Organized Formation of Topologically Correct Feature Maps”, Biological Cybernetics. Pp: 59-69.
  8. H.Lieberman, N.W.Van Dyke, A.S.Vivacqua, “Let’s Browse: A Collaborative Browsing Agent”,pp: 378-385.
  9. M.D.Mulvenna, S.Anand, A.G. Buchner (2000), “Personalization on the net using Web Mining”, Communications of the ACM. pp:123-125.
  10. P.Resnick, N.Iacovou, M.Sushak, P.Bergstrom, J.Reidl (1994), “Grouplens: An Open Architecture for Collaborative Filtering of Netnews”, Proceedings of ACM Conference on Computer Supported Cooperative Work. pp:175-186.
  11. C.Shahabi, F.Banaei-Kashani, Y.S.Chen, D.McLeod (2001), “Yoda: An Accurate and Scalable Web Based Recommendation System”, Proceedings of the 9th International Conference on Cooperative Information Systems, pp:418-432.
  12. Y.Shih, R.Liu (2005), “Hybrid Recommendation Approaches: Collaborative Filtering via Valuable Content Information”, Proceedings of the 38th Hawaii International Conference on System Sciences, pp: 217b.
  13. UCL,http://www.ucl.ac.uk/ontology/Microcore/HTML_resource/SOM_Intro.htm.
  14. Willamette, http://www.willamette.edu/~gorr/classes/cs449/Unsupervised/SOM .html.
  15. Xuejun Zhang, John Edwards, Jenny Harding (2007), “ Personalised Online Sales using Web Usage Data Mining”, Computers in Industry. pp:772-782.
Index Terms

Computer Science
Information Sciences

Keywords

SASF crawler WebUsage Mining pattern discovery FAB