Abstract
To resolve the shortage of traditional clustering algorithm when dealing data set with complex distribution, a novel adaptive k-Nearest Neighbors clustering(AKNNC) algorithm is presented in this paper. This algorithm is made up of three parts: (a)normalize data set; (b)construct initial patterns; (c)merge initial patterns. Simulation results show that compared with classical FCA, our AKNNC algorithm not only has better clustering performance for data set with Complex distribution, but also can be applied to the data set without knowing cluster number in advance.
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References
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, San Diego, CA (1990)
Baraldi, F., Parmiggiani, F.: Fuzzy-shell Clustering and Applications to Circle Detection in Digital Images. Int. J. General Syst 16, 343–355 (1995)
Frigui, H., Krishnapuram, R.: A Comparison of Fuzzy Shell-clustering Method for the De-tection of Ellipses. IEEE Transactions on Fuzzy System 4, 193–199 (1996)
Hubert, L.J.: Some Applications of Graph Theory to Clustering. Psychonmetrika 4, 435–475 (1974)
Liu, Y.T., Shiueng, B.Y.: A Genetic Algorithm for Data with Non-spherical-shape Clusters. Pattern Recognition 33, 1251–1259 (2000)
Patrick, K.S.: Fuzzy Min-Max Neural Networks-Part1: Classification. IEEE Transactions On Neural Networks 3, 776–786 (1992)
Huang, X.B., Wan, J.W., Wang, Z.: A Recursive Algorithm for Computing the Trace of the Sample Covariance Matrix. Pattern Recognition and Artificial Intelligence 17, 497–501 (2004)
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© 2007 Springer-Verlag Berlin Heidelberg
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Zhang, Y., Jia, Y., Huang, X., Zhou, B., Gu, J. (2007). An Adaptive k-Nearest Neighbors Clustering Algorithm for Complex Distribution Dataset. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_44
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DOI: https://doi.org/10.1007/978-3-540-74205-0_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74201-2
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