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.