Interval Set Clustering of Web Users using Modified
	Kohonen Self-Organizing Maps based on the Properties 
			of Rough Sets

	Pawan Lingras, Mofreh Hogo, and Miroslav Snorek


Abstract:

Web usage mining involves application of data mining techniques to 
discover usage patterns from the web data. Clustering is one of the 
important functions in web usage mining. The likelihood of bad or 
incomplete web usage data is higher than the conventional applications. 
The clusters and associations in web usage mining do not necessarily have 
crisp boundaries. Researchers have studied the possibility of using fuzzy 
sets in web mining clustering applications. Recent attempts have adapted 
the K-means clustering algorithm as well as genetic algorithms based on 
rough sets to find interval sets of clusters. The genetic algorithms based 
clustering may not be able to handle large amounts of data. The K-means 
algorithm does not lend itself well to adaptive clustering. This paper 
proposes an adaptation of Kohonen self-organizing maps based on the 
properties of rough sets, to find the interval sets of
clusters. Experiments are used to create interval set representations of 
clusters of web visitors on three educational web sites.

Keywords: Clustering, Interval Sets, Kohonen Self-organizing Maps, 
Web Usage Mining, Rough Sets, Unsupervised Learning.