Different users have different needs from the same web page and hence it is necessary to develop a system which understands the needs and demands of the users. Web server logs have abundant information about the nature of users accessing it. In this paper we discussed how to mine these web server logs for a given period of time using unsupervised and competitive learning algorithm like Kohonen''s self organizing maps (SOM) and interpreting those results using Unified distance Matrix (U-matrix). These algorithms help us in efficiently clustering users based on similar web access patterns and each cluster having users with similar browsing patterns. These clusters are useful in web personalization so that it communicates better with its users and also in web traffic analysis for predicting web traffic at a given period of time.
K. Menon and C. H. Dagli, "Web Personalization using Neuro-Fuzzy Clustering Algorithms," Proceedings of the 22nd International Conference of the North American Fuzzy Information Processing Society, 2003, Institute of Electrical and Electronics Engineers (IEEE), Jan 2003.
The definitive version is available at https://doi.org/10.1109/NAFIPS.2003.1226840
22nd International Conference of the North American Fuzzy Information Processing Society, 2003
Engineering Management and Systems Engineering
Keywords and Phrases
Internet; Kohonen Self Organizing Maps; SOM; U Matrix; Web Access Patterns; Web Design; Web Page; Web Personalization; Web Server Logs; Web Traffic Analysis; Competitive Learning Algorithm; File Servers; Neuro-Fuzzy Clustering Algorithms; Online Front-Ends; Search Engines; Self-Organising Feature Maps; Unified Distance Matrix; Unsupervised Learning; Unsupervised Learning Algorithm
Article - Conference proceedings
© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.