Neural Network Approach for Web Usage Mining
Ketki Muzumdar1, Ravi Mante2, Prashant Chatur3
1Ketki Muzumdar, Department of Computer Science & Engineering, Govt. College of Engineering Amravati, Amravati (Maharashtra), India.
2Ravi Mante, Department of Computer Science & Engineering, Govt. College of Engineering Amravati, Amravati (Maharashtra), India.
3Dr. Prashant Chatur , Department of Computer Science & Engineering, Govt. College of Engineering Amravati, Amravati (Maharashtra), India.
Manuscript received on 21 May 2013 | Revised Manuscript received on 28 May 2013 | Manuscript published on 30 May 2013 | PP: 46-50 | Volume-2 Issue-2, May 2013 | Retrieval Number: B0580052213/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, business and support services, personalization, and network traffic flow analysis and so on. Previous study on Web usage mining using a concurrent Clustering, Neural based approach has shown that the usage trend analysis very much depends on the performance of the clustering of the number of requests. In this paper, a novel approach Self Organizing Map is introduced, which is a kind of neural network, in the process of Web Usage Mining to detect user’s patterns. We are going to analyze the traditional K-Means algorithm result with comparison to SOM. The process details the transformations necessaries to modify the data storage in the Web Servers Log files to an input of SOM.
Keywords: Clustering, K-Means, SOM, Web Server Log File, Web Usage Mining.
Scope of the Article: Web Mining