Temporal Web usage mining involves application of data mining techniques on temporal Web usage data to discover temporal usage patterns, which describe the temporal behavior of users on the Internet Web site, to understand the temporal users' behavior during different time slices. Clustering and classification are two important functions in Web mining. Classes, and associations in Web mining do not necessarily have crisp boundaries. Therefore the conventional clustering techniques became unsuitable to find such clusters and associations, where these conventional classification algorithms provide crisp classes, which are not suitable in real world applications. This gives the chance of using the non-conventional clustering techniques as fuzzy and rough sets in Web mining clustering applications. Recent research introduced the adaptation of Kohonen SOM based on the properties of rough sets theory to find the interval set clusters for the users on the Internet. This paper introduces the comparison between the latest snapshot Web usage mining and the temporal Web usage mining, and. the comparison between the temporal Web usage mining using the conventional Kohonen SOM and the modified Kohonen SOM based on the properties of sets theory. |
