Correlation-Based Web Document Clustering for Adaptive Web Interface Design |
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Authors: | Zhong Su Qiang Yang Hongjiang Zhang Xiaowei Xu Yu-Hen Hu Shaoping Ma |
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Affiliation: | (1) State Key Lab of Intelligent Tech. and Systems, Tsinghua University, Beijing, China, CN;(2) School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, CA;(3) Microsoft Research China, Beijing, China, CN;(4) Siemens AG, Information and Communications Corporate Technology, Munich, Germany, DE;(5) Department of Electrical and Computer Engineering, University of Wisconsin–Madison, Madison, Wisconsin, USA, US |
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Abstract: | A great challenge for web site designers is how to ensure users' easy access to important web pages efficiently. In this
paper we present a clustering-based approach to address this problem. Our approach to this challenge is to perform efficient
and effective correlation analysis based on web logs and construct clusters of web pages to reflect the co-visit behavior
of web site users. We present a novel approach for adapting previous clustering algorithms that are designed for databases
in the problem domain of web page clustering, and show that our new methods can generate high-quality clusters for very large
web logs when previous methods fail. Based on the high-quality clustering results, we then apply the data-mined clustering
knowledge to the problem of adapting web interfaces to improve users' performance. We develop an automatic method for web
interface adaptation: by introducing index pages that minimize overall user browsing costs. The index pages are aimed at providing
short cuts for users to ensure that users get to their objective web pages fast, and we solve a previously open problem of
how to determine an optimal number of index pages. We empirically show that our approach performs better than many of the
previous algorithms based on experiments on several realistic web log files.
Received 25 November 2000 / Revised 15 March 2001 / Accepted in revised form 14 May 2001 |
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Keywords: | : Adaptive web user interfaces Clustering Data mining Web log mining |
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