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Web Usage Analysis and User Profiling [electronic resource] :International WEBKDD’99 Workshop San Diego, CA, USA, August 15, 1999 Revised Papers /

Contributor(s): Masand, Brij [editor.] | Spiliopoulou, Myra [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 1836Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2000.Description: V, 182 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540449348.Subject(s): Computer science | Data structures (Computer science) | User interfaces (Computer systems) | Artificial intelligence | Computers and civilization | Computer Science | Data Structures, Cryptology and Information Theory | Artificial Intelligence (incl. Robotics) | Popular Computer Science | Information Systems Applications (incl. Internet) | User Interfaces and Human Computer Interaction | Computers and SocietyOnline resources: Click here to access online
Contents:
Modelling the Users -- Inferring Demographic Attributes of Anonymous Internet Users -- A Generalization-Based Approach to Clustering of Web Usage Sessions -- Constructing Web User Profiles: A Non-invasive Learning Approach -- Data Mining the Internet and Privacy -- Discovering Rules and Patterns of Navigation -- User-Driven Navigation Pattern Discovery from Internet Data -- Data Mining of User Navigation Patterns -- Making Web Servers Pushier -- Measuring Interestingness in Web Usage Mining -- Analysis and Visualization of Metrics for Online Merchandising -- Improving the Effectiveness of a Web Site with Web Usage Mining -- Discovery of Interesting Usage Patterns from Web Data.
In: Springer eBooksSummary: After the advent of data mining and its successful application on conventional data, Web-related information has been an appropriate and increasingly popular target of knowledge discovery. Depending on whether the data used in the knowledge discovery process concerns the Web itself in terms of content or the usage of the content, one distinguishes between Web content mining and Web usage mining. This book is the first one entirely devoted to Web usage mining. It originates from the WEBKDD'99 Workshop held during the 1999 KDD Conference. The ten revised full papers presented together with an introductory survey by the volume editors documents the state of the art in this exciting new area. The book presents topical sections on Modeling the User, Discovering Rules and Patterns of Navigation, and Measuring interestingness in Web Usage Mining.
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Modelling the Users -- Inferring Demographic Attributes of Anonymous Internet Users -- A Generalization-Based Approach to Clustering of Web Usage Sessions -- Constructing Web User Profiles: A Non-invasive Learning Approach -- Data Mining the Internet and Privacy -- Discovering Rules and Patterns of Navigation -- User-Driven Navigation Pattern Discovery from Internet Data -- Data Mining of User Navigation Patterns -- Making Web Servers Pushier -- Measuring Interestingness in Web Usage Mining -- Analysis and Visualization of Metrics for Online Merchandising -- Improving the Effectiveness of a Web Site with Web Usage Mining -- Discovery of Interesting Usage Patterns from Web Data.

After the advent of data mining and its successful application on conventional data, Web-related information has been an appropriate and increasingly popular target of knowledge discovery. Depending on whether the data used in the knowledge discovery process concerns the Web itself in terms of content or the usage of the content, one distinguishes between Web content mining and Web usage mining. This book is the first one entirely devoted to Web usage mining. It originates from the WEBKDD'99 Workshop held during the 1999 KDD Conference. The ten revised full papers presented together with an introductory survey by the volume editors documents the state of the art in this exciting new area. The book presents topical sections on Modeling the User, Discovering Rules and Patterns of Navigation, and Measuring interestingness in Web Usage Mining.

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