Abstract
Through analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, Rough kernel k-means clustering algorithm with adaptive parameters, is proposed for clustering analysis in this paper. By using Mercer kernel functions, we can map the data in the original space to a high-dimensional feature space, in which we can use rough k-means with adaptive parameters to perform clustering in feature space. Efficiently. The results of simulation experiments show the feasibility and effectiveness of the kernel clustering algorithm.
Sponsored by Ningxia Health Department Scientific Research Fund(2011033), Shaanxi Province Education Department Scientific Research Fund(2010JK466) and Ningxia Medical University Special Talent Scientific Research Start Fund, Ningxia Hui Autonomous Region Natural Sciences Research Fund(NZ11105).
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Zhou, T., Lu, H., Yang, D., Ma, J., Tuo, S. (2011). Rough Kernel Clustering Algorithm with Adaptive Parameters. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_75
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DOI: https://doi.org/10.1007/978-3-642-23896-3_75
Publisher Name: Springer, Berlin, Heidelberg
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