Skip to main content
SpringerLink
Log in

Learning fuzzy concept hierarchy and measurement with node labeling

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • 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 http://www.ics.uci.edu/~mlearn/MLRepository.html.

  • Burmeister, P. (1996). Formal Concept analysis with ConImp: Introduction to Basic Features, TU Darmstdt, Germany, Technical Report, from http://www.mathematik.tudarmstadt.de/˜burmeister/.

  • 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 

  • Fisher, D. H. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2(2), 139–172.

    Google Scholar 

  • Gennari, J. H., Langley, P., & Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence, 40(1), 11–61.

    Article  Google Scholar 

  • Han, J., & Kamber, M. (2001). Data mining: Concept and techniques. Morgan Kaufmann.

  • Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and applications, Prentice Hall.

  • Mangasarian, L., & Wolberg, W. H. (1990). Cancer diagnosis via linear programming. SIAM News, 23(5), 1–18.

    Google Scholar 

  • Mitchell, T. M. (1997). Machine learning. McGraw-Hill.

  • Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.

    Google Scholar 

  • Talavera, L., & Bejar, J. (2001). Generality-based conceptual clustering with probabilistic concepts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 196–206.

    Article  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Tainan, 33, Sec. 2, Su-Lin Street, Tainan, 70005, Taiwan, Republic of China

    Been-Chian Chien & Ming-Yi Ju

  2. Department of Computer Science and Information Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu Township, Kaohsiung County, 84004, Taiwan, Republic of China

    Chih-Hung Hu

Authors
  1. Been-Chian Chien
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chih-Hung Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming-Yi Ju
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Been-Chian Chien.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chien, BC., Hu, CH. & Ju, MY. Learning fuzzy concept hierarchy and measurement with node labeling. Inf Syst Front 11, 551–559 (2009). https://doi.org/10.1007/s10796-008-9126-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-008-9126-z

Keywords

Search

Navigation