Analysis and design of server informative WWW-sites

The access patterns of the users on a web site are traditionally investigated in order to improve the user access to the site s information In this study however a systematic approach is introduced in order to analyze the users navigation path to the advantage of the web site owner As users navigate through a web site they are transparently lling a questionnaire generated by the web site owner We rst cluster the users who navigate similar paths employing the Path Mining algorithm Next the correlation between a set of target questions and the structure of the WWW site is quanti ed This has been done by borrowing the concept of channel from information theory A channel can be considered as an information bridge between the users path classes and the answers to a questionnaire By adopting many concepts from information theory we introduce a natural measure to compute the e ectiveness of a WWW site structure in answering the target questionnaire Using this measure we provide a set of design guidelines to make WWW sites more informative for the server To nd the parameters of a channel we propose a learning process based on a set of training data and or inputs from a human expert Finally our proposed approach is tested on a sample WWW site and the results demonstrate dramatic improvement in the server information passing Author s work supported by NSF grant EEC IMSC ERC

[1]  Gwen Gregory,et al.  World Wide Web Page Design: A Structured Approach , 1997 .

[2]  Cyrus Shahabi,et al.  Knowledge discovery from users Web-page navigation , 1997, Proceedings Seventh International Workshop on Research Issues in Data Engineering. High Performance Database Management for Large-Scale Applications.

[3]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[4]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.

[5]  James Kelly,et al.  AutoClass: A Bayesian Classification System , 1993, ML.

[6]  Umeshwar Dayal,et al.  From User Access Patterns to Dynamic Hypertext Linking , 1996, Comput. Networks.

[7]  Thomas G. Dietterich,et al.  Readings in Machine Learning , 1991 .

[8]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[9]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[10]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[11]  Ben Shneiderman,et al.  Designing information-abundant web sites: issues and recommendations , 1997, Int. J. Hum. Comput. Stud..

[12]  Evangelos Simoudis,et al.  An Overview of Issues in Developing Industrial Data Mining and Knowledge Discovery Applications , 1996, KDD.