TITLE: Webmining: Learning from the World Wide Web

AUTHORS: Jan Larsen, Lars Kai Hansen, Anna Szymkowiak, Torben Christiansen and Thomas Kolenda
Department of Mathematical Modelling, Building 321
Technical University of Denmark, DK-2800 Lyngby, Denmark
emails: jl,lkhansen@imm.dtu.dk
www: http://eivind.imm.dtu.dk


Automated analysis of the world wide web is a new challenging area relevant in many applications, e.g., retrieval, navigation and organization of information, automated information assistants, and e-commerce. This paper discusses the use of unsupervised and supervised learning methods for user behavior modeling and content-based segmentation and classification of web pages.

The modeling is based on independent component analysis and hierarchical probabilistic clustering techniques.

Appears in Proc. of NMDM2000, Rome, Italy, Sept. 25-26, 2000.