The World-Wide Web: quagmire or gold mine?
Communications of the ACM
Web usage mining for Web site evaluation
Communications of the ACM
Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
Web log data warehousing and mining for intelligent web caching
Data & Knowledge Engineering - Building web warehouse
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
COMPSAC '00 24th International Computer Software and Applications Conference
Advanced Data Preprocessing for Intersites Web Usage Mining
IEEE Intelligent Systems
Efficient mining method for retrieving sequential patterns over online data streams
Journal of Information Science
Fuzzy Web Ad Selector Based on Web Usage Mining
IEEE Intelligent Systems
Mining web browsing patterns for E-commerce
Computers in Industry
Web usage mining with intentional browsing data
Expert Systems with Applications: An International Journal
A system of agent-based software patterns for user modeling based on usage mining
Interacting with Computers
Retrieving keyworded subgraphs with graph ranking score
Expert Systems with Applications: An International Journal
Identifying web sessions with simulated annealing
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.