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


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.


Concept hierarchy Fuzzy concept hierarchy Clustering Fuzzy entropy Fuzzy confidence 



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


  1. Blake, C., Keogh E., & Merz, C. J. (1998). UCI repository of machine learning database, Irvine, University of California, Department of Information and Computer Science, from
  2. Burmeister, P. (1996). Formal Concept analysis with ConImp: Introduction to Basic Features, TU Darmstdt, Germany, Technical Report, from˜burmeister/.
  3. Chien, B. C., & Liao, S. Y. (2003). Mining categorical concept hierarchies in large databases. Proceedings of 7th World Multiconference on Systemics, Cybernetics and Informatics, 2, 244–249.Google Scholar
  4. Fisher, D. H. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2(2), 139–172.Google Scholar
  5. Gennari, J. H., Langley, P., & Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence, 40(1), 11–61.CrossRefGoogle Scholar
  6. Han, J., & Kamber, M. (2001). Data mining: Concept and techniques. Morgan Kaufmann.Google Scholar
  7. Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and applications, Prentice Hall.Google Scholar
  8. Mangasarian, L., & Wolberg, W. H. (1990). Cancer diagnosis via linear programming. SIAM News, 23(5), 1–18.Google Scholar
  9. Mitchell, T. M. (1997). Machine learning. McGraw-Hill.Google Scholar
  10. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.Google Scholar
  11. Talavera, L., & Bejar, J. (2001). Generality-based conceptual clustering with probabilistic concepts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 196–206.CrossRefGoogle Scholar

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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