A kernel k-means clustering algorithm based on an adaptive Mahalanobis kernel

In this paper, a kernel k-means algorithm based on an adaptive Mahalanobis kernel is proposed. This kernel is built based on an adaptive quadratic distance defined by a symmetric positive definite matrix that changes at each algorithm iteration and takes into account the correlations between variables, allowing the discovery of clusters with non-hyperspherical shapes. The effectiveness of the proposed algorithm is demonstrated through experiments with synthetic and benchmark datasets.