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2007
Springer

Growing Hierarchical Self-Organizing Maps for Web Mining

9 years 5 months ago
Growing Hierarchical Self-Organizing Maps for Web Mining
— Many information retrieval and machine learning methods have not evolved in order to be applied to the Web. Two main problems in applying some machine learning techniques for Web mining are the dynamic and ever-changing nature of Web data and the sheer size of possible dimensions that this data could portray. One such technique, self-organizing maps (SOMs), have been enhanced to deal with these two problems individually. The growing hierarchical self-organizing map can adapt to the dynamic data present on the Web by changing its topology according to the amount of change in input size. In addition, it reduces local dimensionality by splitting features into levels. We extend this model by including bidirectional update propagation over the levels of the hierarchy. We demonstrate the effectiveness of the new approach with a Web-based news coverage example.
Joseph P. Herbert, Jingtao Yao
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where WEBI
Authors Joseph P. Herbert, Jingtao Yao
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