A Significance-Based Graph Model for Clustering Web Documents
Traditional document clustering techniques rely on single-term analysis, such as the widely used Vector Space Model. However, recent approaches have emerged that are based on Graph Models and provide a more detailed description of document properties. In this work we present a novel Significance-based Graph Model for Web documents that introduces a sophisticated graph weighting method, based on significance evaluation of graph elements. We also define an associated similarity measure based on the maximum common subgraph between the graphs of the corresponding web documents. Experimental results on artificial and real document collections using well-known clustering algorithms indicate the effectiveness of the proposed approach.
KeywordsVector Space Model Maximum Common Subgraph Document Part Unique Node Label Simple Weighting Scheme
Unable to display preview. Download preview PDF.
- 1.Schenker, A., Last, M., Bunke, H., Kandel, A.: Clustering of Web Documents Using a Graph Model. In: Antonacopoulos, A., Hu, J. (eds.) Web Document Analysis: Challenges and Opportunities (to appear)Google Scholar
- 2.Hammuda, K.M.: Efficient Phrase-Based Document Indexing for Web-Document Clustering. IEEE, Los Alamitos (2003)Google Scholar
- 3.Schenker, A., Last, M., Bunke, H., Kandel, A.: A Comparison of Two Novel Algorithms for Clustering Web Documents. In: 2nd Int. Workshop of Web Document Analysis, WDA 2003, Edinburgh, UK, August 2003 (2003)Google Scholar