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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Blake, C., Keogh, E., Merz, C.J.: UCI repository of machine learning database, Department of Information and Computer Science,University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Chien, B.C., Liao, S.Y.: Mining Categorical Concept Hierarchies in Large Databases. Proceedings of 7th World Multiconference on Systemics, Cybernetics and Informatics 2, 244–249 (2003)
Burmeister, P.: Formal Concept analysis with ConImp: Introduction to Basic Features. TU Darmstdt, Germany, Technical Report, http://www.mathematik.tudarmstadt.de/
Fisher, D.H.: Knowledge Acquisition via Incremental Conceptual Clustering. Machine Learning 2(2), 139–172 (1987)
Gennari, J.H., Langley, P., Fisher, D.: Models of incremental concept formation. Artificial Intelligence 40(1), 11–61 (1989)
Han, J., Kamber, M.: Data Mining: Concept and Techniques. Morgan Kaufmann, San Francisco (2001)
Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of International Conference on Research in Computational Linguistics (1997)
Klir, G.J., Yuan, B.: Fuzzy sets and fuzzy logic: theory and applications. Prentice-Hall, Englewood Cliffs (1995)
Mangasarian, L., Wolberg, W.H.: Cancer Diagnosis via Linear Programming. SIAM News 23(5), 1–18 (1990)
Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)
Talavera, L., Bejar, J.: Generality-Based Conceptual Clustering with Probabilistic Concepts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 196–206 (2001)
Wu, Z., Palmer, M.: Verb Semantics and Lexical Selection. In: Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)