A Comparison of Dimensionality Reduction Techniques for Web Structure Mining

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 116 - 119
• Main Authors: Chikhi, Nacim Fateh, Rothenburger, Bernard, Aussenac-Gilles, Nathalie
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Information systems > Information retrieval; Information systems > Information retrieval > Evaluation of retrieval results; Information systems > Information systems applications > Data mining; Information systems > World Wide Web > Web applications; Information systems > World Wide Web > Web services; Mathematics of computing > Mathematical analysis > Numerical analysis > Computations on matrices; Mathematics of computing > Probability and statistics > Statistical paradigms; Mathematics of computing > Probability and statistics > Statistical paradigms > Statistical graphics; Theory of computation > Computational complexity and cryptography > Problems, reductions and completeness
• ISBN: 0769530265; 9780769530260
• DOI: 10.1109/WI.2007.6

In many domains, dimensionality reduction techniques have been shown to be very effective for elucidating the underlying semantics of data. Thus, in this paper we investigate the use of various dimensionality reduction techniques (DRTs) to extract the implicit structures hidden in the web hyperlink connectivity. We apply and compare four DRTs, namely, Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Random Projection (RP). Experiments conducted on three datasets allow us to assert the following: NMF outperforms PCA and ICA in terms of stability and interpretability of the discovered structures; the wellknown WebKb dataset used in a large number of works about the analysis of the hyperlink connectivity seems to be not adapted for this task and we suggest rather to use the recent Wikipedia dataset which is better suited.