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Learning fuzzy concept hierarchy and measurement with node labeling

Author

Listed:
  • Been-Chian Chien

    (National University of Tainan)

  • Chih-Hung Hu

    (I-Shou University)

  • Ming-Yi Ju

    (National University of Tainan)

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.

Suggested Citation

  • Been-Chian Chien & Chih-Hung Hu & Ming-Yi Ju, 2009. "Learning fuzzy concept hierarchy and measurement with node labeling," Information Systems Frontiers, Springer, vol. 11(5), pages 551-559, November.
    • Handle: RePEc:spr:infosf:v:11:y:2009:i:5:d:10.1007_s10796-008-9126-z
    DOI: 10.1007/s10796-008-9126-z
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    Cited by:

    1. Claudia Diamantini & Domenico Potena & Emanuele Storti, 2013. "A virtual mart for knowledge discovery in databases," Information Systems Frontiers, Springer, vol. 15(3), pages 447-463, July.
    2. Qian Wang & Jijun Yu & Weiwei Deng, 2019. "An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations," Electronic Commerce Research, Springer, vol. 19(1), pages 59-79, March.

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