E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Data mining standards, services, and platforms 2004 (DM-SSP 2004)
ACM SIGKDD Explorations Newsletter
Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree
Data Mining and Knowledge Discovery
Data mining standards, services and platforms 2005 workshop report
ACM SIGKDD Explorations Newsletter
Learning from communication data: language in electronic business negotiations
Learning from communication data: language in electronic business negotiations
WMHAS model for improvement document classification
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
Web document classification and its performance evaluation
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
Web metrics for digital influence measurement
DIWEB'08 Proceedings of the 8th WSEAS international conference on Distance learning and web engineering
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In Internet business, Web audience analysis is essential to understanding the visitors' needs. However, the existing analysis tools fail to deliver summarized and conceptual metrics needed by organization managers and Web site editors. The reason is that HTTP transaction metadata mined by these tools do not include the text content sent to the browsers. In this paper, we describe a technique framework for Intelligent E-Business applications that we conceived to mine the Web pages output by Web servers. Web mining consists of extracting knowledge from huge volumes of data available in WWW and other Web sources, allowing better business decisions to be taken. These Web Mining methods include content journaling, script parsing, server monitoring, network monitoring, and client-side mining. Finally, we are achieved our approach through a model framework (WMIB) that has the capability to offer unified heterogeneous data in a repository of data for applying different techniques from web data mining in the Intelligent E-Business domain.