Abstract
This paper proposes a novel kernel clustering algorithm using a hybrid memetic algorithm for clustering complex, unlabeled, and linearly non-separable datasets. The kernel function can transform nonlinear data into a high dimensional feature space. It increases the probability of the linear separability of the patterns within the transformed space and simplifies the associated data structure. According to the distribution of various datasets, three local learning operators are designed; meanwhile double mutation operators incorporated into local learning operators to further enhance the ability of global exploration and overcome premature convergence effectively. The performance comparisons of the proposed method with k-means, kernel k-means, global kernel k-means and spectral clustering algorithms on artificial datasets and UCI datasets indicate that the proposed clustering algorithm outperforms the compared algorithms.
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Acknowledgments
This work was supported by the Program for New Century Excellent Talents in University (No. NCET-12-0920), the National Natural Science Foundation of China (Nos. 61272279, 61001202 and 61203303), the China Postdoctoral Science Foundation Funded Project (Nos. 20080431228, 20090461283 and 20090451369), the China Postdoctoral Science Foundation Special Funded Project (Nos. 200801426 and 201104618), the Fundamental Research Funds for the Central Universities (Nos. K50511020014, K50511020011 and K50510020011), the Provincial Natural Science Foundation of Shaanxi of China (No. 2011JQ8020) and the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048).
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Li, Y., Li, P., Wu, B. et al. Kernel clustering using a hybrid memetic algorithm. Nat Comput 12, 605–615 (2013). https://doi.org/10.1007/s11047-013-9365-x
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DOI: https://doi.org/10.1007/s11047-013-9365-x