Abstract
High quality clustering is impossible without a priori information about clustering criteria. The paper describes the development of new clustering technique based on chaotic neural networks that overcomes the indeterminacy about number and topology of clusters. Proposed method of weights computation via Delaunay triangulation allows to cut down computing complexity of chaotic neural network clustering.
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Kumar, B.V.K.V., Mahalanobis, A., Juday, R.D.: Correlation Pattern Recognition, p. 402. Cambridge University Press, Cambridge (2006)
Valente de Oliveira, J., Pedrycz, W.: Advances in Fuzzy Clustering and its Applications, p. 454. Wiley, Chichester (2007)
Dimitriadou, E., Weingessel, A., Hornik, K.: Voting-Merging: An Ensemble Method for Clustering. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 217–224. Springer, Heidelberg (2001)
Haken, H.: Synergetics. Introduction and Advanced Topics. In: Physics and Astronomy Online Library, p. 758. Springer, Heidelberg (2004)
Mosekilde, E., Maistrenko, Y., Postnov, D.: Chaotic synchronization. World Scientific Series on Nonlinear Science, Series A 42, 440 (2002)
Pikovsky, A., Rosenblum, M., Kurths, J.: Synchronization: A Universal Concept in Nonlinear Sciences. Cambridge University Press, Cambridge (2003)
Osipov, G.V., Kurths, J., Zhou, C.: Synchronization in Oscillatory Networks. Springer Series in Synergetics, p. 370. Springer, Heidelberg (2007)
Han, J., Kamber, M.: Data Mining. Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems, p. 800. Morgan Kaufmann, San Francisco (2005)
Schweitzer, F.: Self-Organization of Complex Structures: From Individual to Collective Dynamics, p. 620. CRC Press, Boca Raton (1997)
Haykin, S.: Neural Networks. A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River (1998)
Kaneko, K.: Phenomenology of spatio-temporal chaos. Directions in chaos, pp. 272–353. World Scientific Publishing Co., Singapore (1987)
Kaneko, K.: Chaotic but regular posinega switch among coded attractors by clustersize variations. Phys. Rev. Lett. N63(14), 219–223 (1989)
Angelini, L., Carlo, F., Marangi, C., Pellicoro, M., Nardullia, M., Stramaglia, S.: Clustering data by inhomogeneous chaotic map lattices. Phys. Rev. Lett. N85, 78–102 (2000)
Angelini, L., Carlo, F., Marangi, C., Pellicoro, M., Nardullia, M., Stramaglia, S.: Clustering by inhomogeneous chaotic maps in landmine detection. Phys. Rev. Lett. N86, 89–132 (2001)
Angelini, L.: Antiferromagnetic effects in chaotic map lattices with a conservation law. Physics Letters A 307(1), 41–49 (2003)
Preparata, F.R., Shamos, M.I.: Computational Geometry. An Introduction, Monographs in Computer Science, p. 398. Springer, Heidelberg (1993)
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Benderskaya, E.N., Zhukova, S.V. (2008). Clustering by Chaotic Neural Networks with Mean Field Calculated Via Delaunay Triangulation. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_51
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DOI: https://doi.org/10.1007/978-3-540-87656-4_51
Publisher Name: Springer, Berlin, Heidelberg
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