Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Measuring similarity of interests for clustering web-users
ADC '01 Proceedings of the 12th Australasian database conference
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Clustering of Web Users Using Session-Based Similarity Measures
ICCNMC '01 Proceedings of the 2001 International Conference on Computer Networks and Mobile Computing (ICCNMC'01)
Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage
Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage
International Journal of Remote Sensing
Clustering Method Based on Fuzzy Multisets for Web Pages and Customer Segments
ISBIM '08 Proceedings of the 2008 International Seminar on Business and Information Management - Volume 02
Personalized Services Research Based on Web Data Mining Technology
ISCID '09 Proceedings of the 2009 Second International Symposium on Computational Intelligence and Design - Volume 02
Dynamic mining of users interest navigation patterns using naive Bayesian method
ICCP '10 Proceedings of the Proceedings of the 2010 IEEE 6th International Conference on Intelligent Computer Communication and Processing
Hi-index | 0.00 |
Everyday a huge amount of pages are published on the Web, and, as a consequence, the users' difficulty to locate those that will meet their needs is increasingly bigger. The challenge for web designers and e-commerce companies is to identify groups of users that present similar interests in order to personalize navigation environments to meet those interests. In an attempt to offer that to the countless web users, in the last years, several researches have been done on clustering applied to Web Usage Mining. In this paper, a log file is preprocessed to map the sequence of visits for each user's session. A Session-Path Matrix is used as input to SOM Map and identifying patterns between each session. The results show the similarities between the sessions based on time spent on visited paths and volume transferred.