Kernel clustering using a hybrid memetic algorithm

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.