Document Clustering with Cluster Refinement and Non-negative Matrix Factorization

Authors:
Sun Park;Dong Un An;Byungrea Char;Chul-Won Kim
Affiliations:
Advanced Graduate Education Center of Jeonbuk for Electronics and Information Technology-BK21, Chonbuk National University, Korea;Division of Electronic & Infomation Engineering, Chonbuk National University, Korea;Network Media Lab., GIST, Korea;Department of Computer Engineering, Honam University, Korea
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Year:
2009

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Abstract

Document clustering is an important method for document analysis and is used in many different information retrieval applications. This paper proposes a new document clustering method using the clustering method based NMF (Non-negative Matrix Factorization) and refinement of documents in clusters by using coherence of cluster. The proposed method can improve the quality of document clustering because the re-assigned documents in cluster by using coherence of cluster based similarity between documents, the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set better. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.