Classification of web documents using graph matching

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 18, Issue 3, 2004, Pages 475-496

  Schenker, Adam ^a   Last, Mark ^b   Bunke, Horst ^c   Kandel, Abraham ^a,d  

^a University of South Florida   (United States)

^b BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^c UNIVERSITY OF BERN   (Switzerland)

^d TEL AVIV UNIVERSITY   (Israel)

Author keywords

Document classification; Graph matching; Graph representation; k nearest neighbors algorithm

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA REDUCTION; GRAPH THEORY; MATHEMATICAL MODELS; ROBUSTNESS (CONTROL SYSTEMS); VECTORS; WORLD WIDE WEB;

DOCUMENT CLASSIFICATION; GRAPH MATCHING; GRAPH REPRESENTATION; K-NEAREST NEIGHBOR ALGORITHM; WEB DOCUMENTS;

PATTERN RECOGNITION;

EID: 3142668315     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001404003241     Document Type: Article

References (18)

1. C. Apte, F. Damerau and S. M. Weiss, Automated learning of decision rules for text categorization, ACM Trans. Inform. Syst. 12 (1994) 233-251.
2. H. Bunke, On a relation between graph edit distance and maximum common subgraph, Patt. Recogn. Lett. 18 (1997) 689-694.
3. H. Bunke, Recent developments in graph matching, in Proc. 15th Int. Conf. Pattern Recognition 2 (2000) 117-124.
4. H. Bunke and K. Shearer, A graph distance metric based on the maximal common subgraph, Patt. Recogn. Lett. 19 (1998) 255-259.
5. M. Crochemore and R. Vèrin, Direct construction of compact directed acyclic word graphs, in CPM97, eds. A. Apostolico and J. Hein (Springer-Verlag, 1997).
6. M.-L. Fernández and G. Valiente, A graph distance metric combining maximum common subgraph and minimum common supergraph, Patt. Recogn. Lett. 22 (2001) 753-758.
7. M. Lazarescu, H. Bunke and S. Venkatesh, Graph matching: fast candidate elimination using machine learning techniques, Advances in Pattern Recognition, Joint IAPR International Workshops SSPR and SPR 2000, Lecture Notes in Computer Science, Vol. 1876, 2000, pp. 236-245.
8. J. Liang and D. Doermann, Logical labeling of document images using layout graph matching with adaptive learning, in Document Analysis Systems V, eds. D. Lopresti, J. Hu and R. Kashi, Lecture Notes in Computer Science, Vol. 2423 (Springer-Verlag, 2002), pp. 224-235.
9. D. Lopresti and G. Wilfong, Applications of graph probing to web document analysis, in Proc. 1st Int. Workshop Web Document Analysis (WDA2001), 2001, pp. 51-54.
10. X. Lu, Document retrieval: a structural approach, Inform. Process. Manag. 26 (1990) 209-218.
11. A. McCallum and K. Nigam, A comparison of event models for naive Bayes text classification, AAAI-98 Workshop on Learning for Text Categorization, 1998.
12. B. T. Messmer and H. Bunke, A new algorithm for error-tolerant subgraph isomorphism detection, IEEE Trans. Patt. Anal. Mach. Intell. 20 (1998) 493-504.
13. T. M. Mitchell, Machine Learning (McGraw-Hill, Boston, 1997).
14. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach (Prentice-Hall, Upper Saddle River, 1995).
15. G. Salton, Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer (Addison-Wesley, Reading, 1989).
16. A. Schenker, M. Last, H. Bunke and A. Kandel, Clustering of web documents using a graph model, in Web Document Analysis: Challenges and Opportunities, eds. A. Antonacopoulos and J. Hu (World Scientific, 2003), pp. 3-8.
17. C.-M. Tan, Y.-F. Wang and C.-D. Lee, The use of bigrams to enhance text categorization, Inform. Process. Manag. 38 (2002) 529-546.
18. W. D. Wallis, P. Shoubridge, M. Kraetz and D. Ray, Graph distances using graph union, Patt. Recogn. Lett. 22 (2001) 701-704.