Granular Computing for Text Mining: New Research Challenges and Opportunities

  • Authors:
  • Liping Jing;Raymond Y. Lau

  • Affiliations:
  • School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China 100044;Department of Information Systems, City University of Hong Kong, Hong Kong

  • Venue:
  • RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
  • Year:
  • 2009

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Abstract

As an emerging computational methodology, granular computing provides an effective strategy for solving many real world problems such as mining latent relationships from text. This paper examines the relationship between granular computing and text mining from a theoretical perspective. Firstly, we analyzes the granular structure of text data which is the key step for textual data representation. Secondly, some granule-based computational methods are described, especially on term-document and document-document similarity calculations. Finally, we highlight several potential research areas where the performance of text mining could be enhanced by applying the concepts of granular computing.