International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 2- Issue 12, December 2013 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Web Site Visit Forecasting Using Data Mining Techniques

[Full Text]



Chandana Napagoda



Keywords: Forecasting, Web Site, SMO Regression, Linear Regression, Gaussian Regression and Multilayer Perceptron



Abstract: Data mining is a technique which is used for identifying relationships between various large amounts of data in many areas including scientific research, business planning, traffic analysis, clinical trial data mining etc. This research will be researching applicability of data mining techniques in web site visit prediction domain. Here we will be concentrating on time series regression techniques which will be used to analyse and forecast time dependent data points. Then how those techniques will be applied to forecast web site visits will be explained.



[1] D. Ciobanu, C. E. Dinuca, “Predicting the next page that will be visited by a web surfer using Page Rank algorithm,” in International Journal of Computers and Communications, 2012, pp.60-67

[2] Z. Markov, D. T. Larose, Data Mining The Web Uncovering Patterns in Web Content, Structure and Usage. USA: John Wiley & Sons, 2007.

[3] T. Wang and Y. Ren, “Research on personalized recommendation based on web usage mining using collaborative filtering technique,” WSEAS Trans. Info. Sci. and App., vol. 6, no. 1, pp. 62–72, Jan. 2009.

[4] X. Wang, A. Abraham, and K. A. Smith, “Intelligent web traffic mining and analysis,” J. Netw. Comput. Appl., vol. 28, no. 2, pp. 147–165, Apr. 2005.

[5] W. Tong and H. Pi-lian, Web Log Mining by an Improved AprioriAll Algorithm, ;in Proc. WEC (2), 2005, pp.97-100.

[6] I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, 3rd ed. Morgan Kaufmann, 2011.

[7] K. Driessens, “GaussianProcesses.” [Online]. Available: http://weka.sourceforge.net/doc.dev/index.html?weka/classifiers/functions/GaussianProcesses.html. [Accessed: 6-Aug-2012].

[8] M. Ware, “MultilayerPerceptron.” [Online]. Available: http://weka.sourceforge.net/doc/weka/classifiers/functions/MultilayerPerceptron.html. [Accessed: 8-Aug-2012].

[9] E. Frank and L. Trigg, “LinearRegression.” [Online]. Available: http://weka.sourceforge.net/doc/weka/classifiers/functions/LinearRegression.html. [Accessed: 10-Aug-2012].

[10] E. Frank, L. Shane, and S. Inglis, “SMO.” [Online]. Available: http://weka.sourceforge.net/doc/weka/classifiers/functions/SMO.html. [Accessed: 7-Aug-2012].

[11] M. Hall, “Time Series Analysis and Forecasting with Weka - Pentaho Data Mining.” [Online]. Available: http://wiki.pentaho.com/display/DATAMINING/Time+Series+Analysis+and+Forecasting+with+Weka. [Accessed: 10-Aug-2012].