Nearest Neighbour Equilibrium Clustering
This provides an automated clustering method for data analysis, but it appears incremental as it builds on existing nearest neighbors approaches.
The authors tackled the problem of clustering by introducing a nearest neighbors-based algorithm that defines clusters through an equilibrium condition balancing size and cohesiveness, resulting in high-quality clustering solutions compared to benchmarks.
A novel and intuitive nearest neighbours based clustering algorithm is introduced, in which a cluster is defined in terms of an equilibrium condition which balances its size and cohesiveness. The formulation of the equilibrium condition allows for a quantification of the strength of alignment of each point to a cluster, with these cluster alignment strengths leading naturally to a model selection criterion which renders the proposed approach fully automatable. The algorithm is simple to implement and computationally efficient, and produces clustering solutions of extremely high quality in comparison with relevant benchmarks from the literature. R code to implement the approach is available from https://github.com/DavidHofmeyr/NNEC.