Kernel k-MACE: hypercube unsupervised clustering method

Transforming data to feature space using a kernel function can result in better expression of its features, resulting in better separability for some datasets. The parameters of the kernel function govern the structure of data in feature space and need to be optimized simultaneously while also estimating the number of clusters in a dataset. The proposed method denoted by kernel k-Minimum Average Central Error (kernel k-MACE), esti- mates the number of clusters in a dataset while simultaneously clustering the dataset in feature space by finding the optimum value of the Gaussian kernel parameter σk. A cluster initialization technique has also been proposed based on an existing method for k-means clustering. Simulations show that for self-generated datasets with Gaus- sian clusters having 10% - 50% overlap and for real benchmark datasets, the proposed method outperforms multiple state-of-the-art unsupervised clustering methods including k-MACE, the clustering scheme that inspired kernel k-MACE.