Web usage mining with intentional browsing data

Many researches have developed Web usage mining (WUM) algorithms utilizing Web log records in order to discover useful knowledge to be used in supporting business applications and decision making. The quality of WUM in knowledge discovery, however, depends on the algorithm as well as on the data. This research explores a new data source called intentional browsing data (IBD) for potentially improving the effectiveness of WUM applications. IBD is a category of online browsing actions, such as ''copy'', ''scroll'', or ''save as,'' and is not recorded in Web log files. Consequently, the research aims to build a basic understanding of IBD which will lead to its easy adoption in WUM research and practice. Specifically, this paper formally defines IBD and clarifies its relationships with other browsing data via a proposed taxonomy. In order to make IBD available like Web log files, an online data collection mechanism for capturing IBD is also proposed and discussed. The potential benefits of IBD can be justified in terms of its enhancing and complementary effectiveness, which are illustrated by the rule implications of Web transaction mining algorithm for an EC application. Introducing IBD opens up the scope of WUM research and applications in knowledge discovery.

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