Web Usage Mining Personalization of Web Usage Data

Abstract: In this paper, we present a complete framework and findings in mining Web usage patterns from Web log files of a real Web site that has all the challenging aspects of real-life Web usage mining, including evolving user profiles and external data describing an ontology of the Web content. we present an approach for discovering and tracking evolving user profiles. We also describe how the discovered user profiles can be enriched with explicit information need that is inferred from search queries extracted from Web log data. Profiles are also enriched with other domain-specific information facets that give a panoramic view of the discovered mass usage modes. paper presents a knowledge discovery framework for the construction of Community Web Directories, a concept that we introduced in our recent work, applying personalization to Web directories. In this context, the Web directory is viewed as a thematic hierarchy and personalization is realized by constructing user community models on the basis of usage data. we enhance the clustering and probabilistic approaches presented in previous work and also present a new algorithm that combines these two approaches. The resulting community models take the form of Community Web Directories. The proposed personalization methodology is evaluated both on a specialized artificial and a general-purpose Web directory, indicating its potential value to the Web user. Web mining techniques seek to extract knowledge from Web data. This paper provides an overview of past and current work in the three main areas of Web mining research— content, structure, and usage as well as emerging work in Semantic Web mining. Statistical testing and reliability analysis can be used effectively to assure quality for Web applications. To support this strategy, we extract Web usage and failure information from existing Web logs. The usage information is used to build models for statistical Web testing. Optimizing components before optimizing the system as a whole can help large organizations deploy efficient, geographically redundant Web infrastructures.

Keywords: Clustering, Mining Evolving Clickstreams, Machine Learning, Personalization, Reliability Analysis, Statistical Testing, Semantic Web Mining, User Profiles, Usage Measurement, Web Usage Mining, Web Mining, World Wide Web.