Abstract
This paper proposes a hybrid rough K-means algorithm for image classification. The rough set theory is used to establish the lower and upper bound for data clustering in the K-means algorithm. Then, the particle swarm optimization (PSO) is employed to optimize the solutions of the rough K-means algorithm. The combined algorithm is called the Rough K-means PSO algorithm. Experimental results show that the proposed algorithm performs better and improves the classification in the blurred and vague areas of test images.
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© 2008 Springer-Verlag Berlin Heidelberg
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Hung, CC., Purnawan, H. (2008). A Hybrid Rough K-Means Algorithm and Particle Swarm Optimization for Image Classification. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_56
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DOI: https://doi.org/10.1007/978-3-540-88636-5_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88635-8
Online ISBN: 978-3-540-88636-5
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