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CLASSIFICATION OF WEB DOCUMENTS USING GRAPH MATCHING.
- Source :
-
International Journal of Pattern Recognition & Artificial Intelligence . May2004, Vol. 18 Issue 3, p475-496. 22p. - Publication Year :
- 2004
-
Abstract
- In this paper we describe a classification method that allows the use of graph-based representations of data. instead of traditional vector-based representations. We compare the vector approach combined with the k-Nearest Neighbor (k-NN) algorithm to the graph-matching approach when classifying three different web document collections, using the leave-one-out approach for measuring classification accuracy. We also compare the performance of different graph distance measures as well as various document representations that utilize graphs. The results show the graph-based approach can outperform traditional vector-based methods in terms of accuracy, dimensionality and execution time. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02180014
- Volume :
- 18
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- International Journal of Pattern Recognition & Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 13063894
- Full Text :
- https://doi.org/10.1142/S0218001404003241