A practical extension of web usage mining with intentional browsing data toward usage

Abstract

Intentional browsing data is a new data component for improving Web usage mining that uses Web log files as the primary data source. Previously, the Web transaction mining algorithm was used in e-commerce applications to demonstrate how it could be enhanced by intentional browsing data on pages with item purchase and complemented by intentional browsing data on pages without item purchase. Although these two intention-based algorithms satisfactorily illustrated the benefits of intentional browsing data on the original Web transaction mining algorithm, three potential issues remain: Why is there a need to separate the source data into purchased-item and not-purchased-item segments to be processed by two intention-based algorithms? Moreover, can the algorithms contain more than one browsing data types? Finally, can the numeric intention-based data counts be more user friendly for decision-making practices? To address these three issues, we propose a unified intention-based Web transaction mining algorithm that can efficiently process the whole data set simultaneously with multiple intentional browsing data types as well as transform the intentional browsing data counts into easily understood linguistic items using the fuzzy set concept. Comparisons and implications for e-commerce are also discussed. Instead of addressing the technical innovation in this extension study, the revised intention-based Web usage mining algorithm should make its applications much easier and more useful in practice.