Research Article | OPEN ACCESS
An Improved Web Log Mining and Online Navigational Pattern Prediction
1D. Anandhi and 2M.S. Irfan Ahmed
1Department of Computer Technology and Applications, Coimbatore Institute of Technology
2Department of Computer Applications, Sri Krishna College of Engineering and Technology, Coimbatore-641008, India
Research Journal of Applied Sciences, Engineering and Technology 2014 12:1472-1479
Received: August 01, 2014 | Accepted: September 22, 2014 | Published: September 25, 2014
Abstract
The aim of this study is to improve web log mining and online navigation pattern prediction. Web mining is an active and wide area which incorporates several usages for the web site design, providing personalization server and other business making decisions etc. Efficient web log mining results and online navigational pattern prediction is a tough process due to vast development in web. It includes the process such as data cleaning, session identification and clustering of web logs generally. In this study initially the web log data is preprocessed and sessions are identified using refined time-out based heuristic for session identification. Then for pattern discovery a density based clustering algorithm is used. Finally for online navigation pattern prediction a new technique of SVM classification is used, which rectifies time complexity with increased prediction accuracy.
Keywords:
DBSCAN , optics , support vector machine , web mining,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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