A Combination Scheme for Fuzzy Clustering-Reference-Cited by-同舟云学术

A Combination Scheme for Fuzzy Clustering

Published:2002 Issue: Volume: Page:332-338
ISSN:0302-9743
Container-title:Advances in Soft Computing — AFSS 2002
language:
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Author:

Dimitriadou Evgenia,Weingessel Andreas,Hornik Kurt

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/3-540-45631-7_44

Reference17 articles.

1. Eric Bauer and Ron Kohavi. An empirical comparison of voting classification algortihms: Bagging, boosting, and variants. Machine Learning, 36:105–139, 1999.

2. James C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms, chapter Cluster Validity, pages 100–120. Plenum Press, N.Y., 1981.

3. Didier Dubois and Henri Prade. Fuzzy Sets and Systems: Theory and Applications, chapter Fuzzy Sets, page 24. Academic Press, Harcourt Brace Jovanovich, 1980.

4. I. Gath and A. B. Geva. Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):773–781, 1989.

5. Janusz Kacprzyk. Fuzzy-set-theoretic approach to the optimal assignment of work places. In G. Guardabassi and A. Locatelli, editors, Large Scale Systems: Theory and Applications, Proceedings of the IFAC Symposium, pages 123–131, Udine, Italy, June 1976.

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