Learning fuzzy concept hierarchy and measurement with node labeling

Authors:
Been-Chian Chien;Chih-Hung Hu;Ming-Yi Ju
Affiliations:
Department of Computer Science and Information Engineering, National University of Tainan, Taiwan, R.O.C.;Department of Information Engineering, I-Shou University, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, National University of Tainan, Taiwan, R.O.C.
Venue:
ISPA'07 Proceedings of the 2007 international conference on Frontiers of High Performance Computing and Networking
Year:
2007

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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 nodelabeling 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.