Abstract
A concept hierarchy is a kind of general form of knowledge representations. Since concept description is generally vague for human knowledge, crisp description for a concept usually cannot represent human knowledge completely and practically. In this paper, we discuss fuzzy characteristics of concept description and relationship. An agglomerative clustering scheme is proposed to learn hierarchical fuzzy concepts from databases automatically. We also propose the architecture of concept measurement and develop two node-labeling methods for measuring the effectiveness of fuzzy concept. Experimental results show that the proposed clustering method demonstrates the capability of accurate conceptualization in comparison with previous researches.
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© 2007 Springer-Verlag Berlin Heidelberg
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Chien, BC., Hu, CH., Ju, MY. (2007). Learning Fuzzy Concept Hierarchy and Measurement with Node Labeling. In: Thulasiraman, P., He, X., Xu, T.L., Denko, M.K., Thulasiram, R.K., Yang, L.T. (eds) Frontiers of High Performance Computing and Networking ISPA 2007 Workshops. ISPA 2007. Lecture Notes in Computer Science, vol 4743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74767-3_18
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DOI: https://doi.org/10.1007/978-3-540-74767-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74766-6
Online ISBN: 978-3-540-74767-3
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