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Prediction of Web Users Browsing Behaviour Using Fast Longest Common Sub-Sequence


Affiliations
1 Department of Computer Application, Sri Ramakrishna Mission Vidyalaya College of Arts and Science, India
2 Sri Ramakrishna Mission Vidyalaya College of Arts and Science, India
     

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As the Web and its usage continues to grow, so grows the opportunity to analyze Web data and extract all manner of useful knowledge from it. The past nine years have seen the emergence of Web mining as a rapidly growing area, due to the efforts of the research community as well as various organizations that are practicing it. The various works proposed in this area with particular emphasize on web usage mining. In the present work, the application of clustering to extract user navigation behaviour pattern is probed and the methods and techniques used are explained in the Methodology. Experiments were conducted on a Pentium IV system with 512MB memory, running in Windows environment. The application was developed in MATLAB 7.3. The results of this study are divided into the following sections: Preprocessing results, Pattern Discovery and Performance Analysis.


Keywords

World Wide Web, Web Usage Mining, Clustering, Classification, Fast Longest Common Subsequence.
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  • Prediction of Web Users Browsing Behaviour Using Fast Longest Common Sub-Sequence

Abstract Views: 271  |  PDF Views: 1

Authors

M. Chandran
Department of Computer Application, Sri Ramakrishna Mission Vidyalaya College of Arts and Science, India
K. Karthika
Sri Ramakrishna Mission Vidyalaya College of Arts and Science, India

Abstract


As the Web and its usage continues to grow, so grows the opportunity to analyze Web data and extract all manner of useful knowledge from it. The past nine years have seen the emergence of Web mining as a rapidly growing area, due to the efforts of the research community as well as various organizations that are practicing it. The various works proposed in this area with particular emphasize on web usage mining. In the present work, the application of clustering to extract user navigation behaviour pattern is probed and the methods and techniques used are explained in the Methodology. Experiments were conducted on a Pentium IV system with 512MB memory, running in Windows environment. The application was developed in MATLAB 7.3. The results of this study are divided into the following sections: Preprocessing results, Pattern Discovery and Performance Analysis.


Keywords


World Wide Web, Web Usage Mining, Clustering, Classification, Fast Longest Common Subsequence.