Fuzzy C-means clustering of web users for educational sites

Authors:
Pawan Lingras;Rui Yan;Chad West
Affiliations:
Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Nova Scotia, Canada;Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Nova Scotia, Canada;Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Nova Scotia, Canada
Venue:
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Year:
2003

Citing 3
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Abstract

Characterization of users is an important issue in the design and maintenance of websites. Analysis of the data from the World Wide Web faces certain challenges that are not commonly observed in conventional data analysis. The likelihood of bad or incomplete web usage data is higher than in conventional applications. The clusters and associations in web mining do not necessarily have crisp boundaries. Researchers have studied the possibility of using fuzzy sets for clustering of web resources. This paper presents clustering using a fuzzy c-means algorithm, on secondary data consisting of access logs from the World Wide Web. This type of analysis is called web usage mining, which involves applying data mining techniques to discover usage patterns from web data. The fuzzy c-means clustering was applied to the web visitors to three educational websites. The analysis shows the ability of the fuzzy c-means clustering to distinguish different user characteristics of these sites.