Earticle

초록

영어

In traditional text clustering, documents appear terms frequency without considering the semantic information of each document (i.e., vector model). The property of vector model may be incorrectly classified documents into different clusters when documents of same cluster lack the shared terms. Recently, to overcome this problem uses knowledge based approaches. However, these approaches have an influence of structure of document set and a cost problem of constructing ontology. In this paper, we propose a text clustering method using semantic terms for clustering label and term weights. The semantic terms of clustering label can well express the internal structure of document clusters using non-negative matrix factorization (NMF). It can also improve the quality of text clustering which uses the term weights by WordNet. The experimental results demonstrate that the proposed method achieves better performance than other text clustering methods.

목차

Abstract
 1 Introduction
 2. Non-negative Matrix Factorization
 3. Proposed Text Clustering Method
  3.1. Preprocessing
  3.2. Extracting Semantic Terms
  3.4. Clustering text document
 4. Experiments
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Sun Park [ Institute Research of Information Science and Engineering, Mokpo National University ]
• Seong Ro Lee [ Department of Information and Electronic, Mokpo Naitional University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605