| 摘要(英) |
In Information Retrieval, document clustering is a technology that can enhance the efficiency in the retrieving of needed information. With document clustering, one can efficiently management all kinds of knowledge and information. Document clustering is a tool for knowledge management.
Traditionally, document clustering is based on document similarity comparison where the document is represented by the vector space model with term as the dimension value. In this approach, the documents with the same semantic meaning might be classified as unsimilar because they are described with different words.In this research, we have integrated the technology of concept extraction with vector space model for document similarity comparison. We extract concepts from the documents first, then create a vector space model with the extracted concepts as the dimension value for the document. Documents similarity comparison is based on the concept-dimensioned vector space model. We wish that the concept based vector space model could enhance the document clustering efficiency.
We have experimented with the document clustering effect for the concept based vector space modle. The results show that the concept based vector space model can perform better than term based vector space model. |
| 參考文獻 |
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