Clustering is a form of unsupervised learning which can partition data into subsets based upon input attributes and distance metrics such as Euclidean. Clustering is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes or do not efficiently solve the cases with overlapping clusters.

This paper introduces a new strategy to clustering based on shape-adaptive potential functions and optimization procedure for positioning of the cluster centers during the learning process. The two fundamental components of SYNNC are potential function generators (PFGs) using symmetrical kernels and...