Large-Scale Mining of Usage Data on Web Sites

Georgios Paliouras, Christos Papatheodorou, Vangelis Karkaletsis, Panayotis Tzitziras, and Constantine D. Spyropoulos

In this paper we present an approach to the discovery of trends in the usage of large Web-based information systems. This approach is based on the empirical analysis of the users’ interaction with the system and the construction of user groups with common interests (user communities). The empirical analysis is achieved with the use of cluster mining, a technique that process data collected from the users’ interaction with the Web site. Our main concern is the construction of meaningful communities, which can be used for improving the structure of the site as well as for making suggestions to the users at a personal level. Our case study on a site providing information for researchers in Chemistry shows that the proposed method provides effective mining of large usage databases.

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