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Published in:
2007 | OriginalPaper | Chapter
Learning Fuzzy Concept Hierarchy and Measurement with Node Labeling
Authors : Been-Chian Chien, Chih-Hung Hu, Ming-Yi Ju
Published in: Frontiers of High Performance Computing and Networking ISPA 2007 Workshops
Publisher: Springer Berlin Heidelberg
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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.