Adaptive fuzzy clustering with a variable fuzzifier
- 62 Downloads
The problem of fuzzy clustering of multivariate observations is considered and a group of Kohonen neural network adaptive self-learning algorithms is proposed. The algorithms allow for online possibilistic fuzzy clustering with variable fuzziness levels and are computationally simple and flexible when operating under a priori uncertainty about the nature of data distribution in clusters.
Keywordsfuzzy clustering fuzzifier Kohonen neural network self-learning algorithm
Unable to display preview. Download preview PDF.
- 2.F. Hoppner, F. Klawonn, R. Kruse, abd T. Runkler, Fuzzy Clustering Analysis: Methods for Classification, Data Analysis, and Image Recognition, Wiley, Chichester (1999).Google Scholar
- 5.J. McQueen, “On convergence of k-means and partitions with minimum average variance,” Ann. Math. Statist., 36, 1084 (1965).Google Scholar
- 7.F. Klawonn and F. Hoppner, “What is fuzzy about fuzzy clustering? Understanding and improving the concept of the fuzzifier,” Lect. Notes Comp. Sci., Springer, Berlin-Heidelberg, 2811, 254–264 (2003).Google Scholar
- 8.S. Mitaim and B. Kosko, “What is the best shape for a fuzzy set in function approximation?” in: Proc. 5th IEEE Int. Conf. on Fuzzy Systems “Fuzzy-96," 2, New Orleans (USA) (1996), pp. 1233–1237.Google Scholar
- 9.S. Mitaim and B. Kosko, “Adaptive joint fuzzy sets for function approximation,” in: Proc. Int. Conf. on Neural Networks “ICNN-97,” Barcelona (Spain) (1997), pp. 537–542.Google Scholar
- 10.D. C. Park and I. Dagher, “Gradient based fuzzy c-means (GBFCM) algorithm,” in: Proc. IEEE Int. Conf. on Neural Networks, San Diego (USA) (1984), pp. 1626–1631.Google Scholar
- 12.Ye. V. Bodyanskii, Ye. V. Gorshkov, I. V. Kokshenev, and V. V. Kolodyazhniy, “An adaptive algorithm for fuzzy clustering of data,” Adaptive Automatic Control Systems, System Technologies, Dnipropetrovsk, No. 5 (25), 108–117 (2002).Google Scholar
- 13.Ye. Bodyanskiy, V. Kolodyazhniy, and A. Stephan, “Recursive fuzzy clustering algorithms,” in: Proc. 10th East West Fuzzy Colloquium, Zittau (Germany) (2002), pp. 276–283.Google Scholar
- 14.Ye. Bodyanskiy, “Computational intelligence techniques for data analysis,” Lect. Notes in Informatics, P-72, 15–36, Bonn (Germany) (2005).Google Scholar
- 18.A. Frank and A. Asuncion, UCI Machine Learning Repository, School of Information and Computer Science, Univ. of California, Irvine (CA) (2010), http://archive.ics.uci.edu/ml.