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1. All document clustering methods are based on keywords now.

2. And then comes the document clustering techniques.

3. To improve document clustering, a document similarity measure based on cosine vector and keywords frequency in documents is proposed, but also with an input ontology.

4. A multi-level document clustering algorithm was proposed based on association rules, It constructed document feature vector of topic and keyword by using a new method of document feature extraction.

5. Experiments show that the automatic parameter setting in document clustering generates more satisfied clustering result and improves the clustering efficiency.

6. The thesis proposes a new document clustering method that uses a model named Document Index Graph to represent Chinese documents.

7. It uses the document clustering to find multi-interests of each user and the accuracy of describing user's interests is improved.

8. Document clustering had been employed in information filtering, web page classification and so on.

9. This paper proposed the algorithm for a document clustering based on similarity to overcome the drawbacks of ordinary information search.

10. Then use Fuzzy C-means to do document clustering based on the results of similarity calculation above.

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11. As an unsupervised machine learning method, document clustering has been widely used in many NLP applications such as information retrieval, automatic multi-document summarization and etc.

12. The task of document clustering is both difficult and intensively studied.

13. This thesis proposes a document clustering method combining ontology and lexical chain clusters to compensate the disadvantage of only using lexical chain.

14. Document clustering description is a problem of labeling the clustered results of document collection clustering.

15. Document clustering is to separate the document set into groups, in each group the documents are of the same or related topic.

16. The experiment results have shown that the hybrid clustering method can improve the document clustering performance.

17. To improve the clustering quality of massive extensible markup language(XML) document collections, this paper proposes a novel XML document clustering method.

18. For new topic detection at the event level, method based on clustering was discussed mainly, which included the representation of documents, and document clustering.

19. This paper presents a method to study interdisciplines by document clustering analysis.

20. According to the requirement of online public opinion analysis, this paper builds an online public opinion hotspot detection and analysis system based on document clustering.