Abstract
A novel neural network, named Associative Clustering Neural Network (ACNN), is developed for clustering data whose underlying distribution shapes are arbitrary. ACNN is a dynamic model that collectively measures and updates the similarity of any two patterns through the interaction of a group of patterns. Such a new measure of similarity helps to achieve more robust clustering performance than using the existing measures that are staticly and individually based on the distances among the isolated pairwise data. The efficience of ACNN has been verified through the performance study.
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References
Duda, R. and Hart, P.: Pattern Classification and Scene Analysis, New York: Interscience, 1973.
Bezdek, J. Z.: Pattern Recognition with Fuzzy Objective Function Algorithms, New York: Plenum, 1981.
Krishnapuram, R. and Keller, J. M.: A Possibilistic Approach to Clustering, IEEE Trans. Fuzzy Syst. 1(2) (1993), 98–110.
Karayiannis, N. B. and Bezdek, J. C.: An Integrated Approach to Fuzzy Learning Vector Quantization and Fuzzy c-means Clustering, IEEE Trans. Fuzzy Syst. 5(4) (1997), 622–628.
Dave, R. N. and Krishnapuram, R.: Robust Clustering Methods: A Unified View, IEEE Trans. Fuzzy Syst. 5(2) (1997), 270–293.
Krishnapuram, R., Frigui, H. and Nasraoui, O.: Fuzzy and Possibilistic Shell Clustering Algorithms and Their Application to Boundary Detection and Surface Approximation, IEEE Trans. Fuzzy Syst. 3(1) (1995), 29–60.
Urquhart, R.: Graph Theoretic Clustering Based on Limited Neighborhood Sets, Pattern Recog. 15 (1982), 173–187.
Wu, Z. and Leahy, R.: An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation, IEEE Trans. Pattern Anal.Machine Intel. 15(11) (1993), 1101–1113.
Eltoft, T. and deFigueiredo, R. J. P.: A New Neural Network for Cluster-Detection and-Labeling, IEEE Trans. Neural Networks 9(5) (1998), 1021–1035.
Wang, J.: A Linear Assignment Clustering Algorithm Based on the Least Similar Cluster Representatives, IEEE Trans. Syst. Man. and Cyber. (Part A) 29(1) (1999), 100–104.
Hofmann, T. and Buhmann, J. M.: Pairwise Data Clustering by Deterministic Annealing, IEEE Trans. Pattern Anala. Machine Intel. 19(1) (1997), 1–14.
Hopfield, J. J.: Neural Network and Physical Systems with Emergent Collective Computational Abilities, Proc. Acad. Sci. U.S.A. 79 (1982), 2554–2558.
Kosko, B.: Adaptive Bidirectional Associative Memories, Appl. Opt. 26(23) (1987), 4947–4960.
Kawamura, M., Okada, M. and Hirai, Y.: Dynamics of Selective Recall in an Associative Memory Model with One-to-Many Associations, IEEE Trans. Neural Networks 10(3) (1999), 704–713.
Hinton, G. E. and Anderson, J. A.: ParallelModels of AssociativeMemory, New Jersey: Hillsidale, 1989.
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Yao, Y., Chen, L. & Chen, Y.Q. Associative Clustering for Clusters of Arbitrary Distribution Shapes. Neural Processing Letters 14, 169–177 (2001). https://doi.org/10.1023/A:1012759532608
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DOI: https://doi.org/10.1023/A:1012759532608