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Information Systems Frontiers

, 11:551 | Cite as

Learning fuzzy concept hierarchy and measurement with node labeling

  • Been-Chian Chien
  • Chih-Hung Hu
  • Ming-Yi Ju
Article

Abstract

A concept hierarchy is a kind of general form of knowledge representation. Most of the previous researches on describing the concept hierarchy use tree-like crisp taxonomy. However, concept description is generally vague for human knowledge; crisp concept description usually cannot represent human knowledge actually and effectively. In this paper, the fuzzy characteristics of human knowledge are studied and employed to represent concepts and hierarchical relationships among the concepts. An agglomerative clustering scheme is proposed to learn hierarchical fuzzy concepts from databases. Further, a novel measurement approach is developed for evaluating the effectiveness of the generated fuzzy concept hierarchy. The experimental results show that the proposed method demonstrates the capability of accurate conceptualization in comparison with previous researches.

Keywords

Concept hierarchy Fuzzy concept hierarchy Clustering Fuzzy entropy Fuzzy confidence 

Notes

Acknowledgments

This work was supported in part by the National Science Council under Grant NSC95-2221-E-024-014

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational University of TainanTainanRepublic of China
  2. 2.Department of Computer Science and Information EngineeringI-Shou UniversityKaohsiung CountyRepublic of China

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