A fuzzy FCA-based approach to conceptual clustering for automatic generation of concept hierarchy on uncertainty data

CEUR Workshop Proceedings

Volumn 110, Issue , 2004, Pages 1-12

  Quan, Thanh Tho ^a   Hui, Siu Cheung ^a   Cao, Tru Hoang ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY   (Viet Nam)

Author keywords

Concept hierarchy; Conceptual clustering; Formal concept analysis; Fuzzy logic

Indexed keywords

COMPUTER CIRCUITS; FUZZY LOGIC; INFORMATION ANALYSIS;

AUTOMATIC GENERATION; CONCEPT HIERARCHIES; CONCEPTUAL CLUSTERING; CONCEPTUAL CLUSTERS; FORMAL CONCEPT ANALYSES (FCA); FUZZY CONCEPT LATTICES; HIERARCHICAL RELATIONS;

FORMAL CONCEPT ANALYSIS;

EID: 84994533747     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. P. Cheeseman, J. Kelly, M. Self, J. Stutz, W. Taylor, and D. Freeman. AutoClass: A Bayesian Classification System. Proceedings of the Fifth International Workshop on Machine Learning Morgan Kauffmann, San Mateo, CA. 1988, pp. 54-64.
2. W. Chu and K. Chiang. Abstraction of High Level Concepts from Numerical Values in Databases. Proceedings of AAAI Workshop on Knowledge Discovery in Databases. 1994, pp. 133-144.
3. D.H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning. Vol. 2. 1987, pp. 139-172.
4. B.Ganter and R. Wille. Formal Concept Analysis: Mathematical Foundations. Springer, Berlin-Heidelberg, 1999.
5. J.H Gennari, P. Langley and Fisher. Models of Incremental Concept Formation, In J.Carbonell, Editor, Machine Learning: Paradigms and Methods. The Netherlands: MIT Press, 1990, pp. 11-62.
6. T.B. Ho. Incremental Conceptual Clustering in the Framework of Galois Lattice". In H. Lu, H. Motoda, H. Luu, editors.KDD: Techniques and Applications, World Scientific. 1997, pp. 49-64.
7. V.N. Huynh and Y. Nakamori. Fuzzy concept formation based on context model", in: N. Baba et al., editors. Knowledge-Based Intelligent Information Engineering Systems & Allied Technologies. IOS Press, Amsterdam, 2001, pp. 687-691.
8. ISI.Institute for Scientific Information. Available at: http://www.isinet.com, 2000.
9. I. Jonyer, D.J. Cook and L.B. Holder. Graph-based hierarchical conceptual clustering. The Journal of Machine Learning Research, Vol. 2. 2002, pp. 19-43.
10. C. Li and G. Biswas. Conceptual Clustering with Numeric and Nominal Mixed Data-A New Similarity Based System. EEE Transactions on Knowledge and Data Engineering. 1996.
11. Y. Lu. Concept Hierarchy in Data Mining: Specification, Generation and Implementation. Technical Report. Available at: http://gunther.smeal.psu.edu/3024.html.
12. N.Nanas, V.Uren and A. de Roeck. Building and Applying a Concept Hierarchy Representation of a User Profile. Proceedings of the 26th annual international ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, 2003.
13. S. Pollandt. Fuzzy-Begriffe: Formale Begriffsanalyse unscharfer Daten, Springer Verlag. Berlin-Heidelberg, 1996.
14. Y. Reich and S.J. Fenves. The formation and use of abstract concepts in design, In D.H. Fisher and M.J. Pazzani, Editors. Concept Formation: Knowledge and Experience in Unsupervised Learning, Morgan Kaufmann. 1991, pp. 323-353.
15. F. Vogt and R. Wille. TOSCANA: a Graphical Tool for Analyzing and Exploring Data, In R.Tamassia and I. G. Tollis, editors. GraphDrawing' 94. Heidelberg, 1995, pp.226-233.
16. L.A Zadeh. Fuzzy Sets. Journal of Information and Control, Vol. 8. 1965, pp. 338-353.