A simple statistics-based nearest neighbor cluster detection algorithm

We propose a new method for autonomously finding clusters in spatial data. The proposed method belongs to the so called nearest neighbor approaches for finding clusters. It is a repetitive technique which produces changing averages and deviations of nearest neighbor distance parameters and results in a final set of clusters. The proposed technique is capable of eliminating background noise, outliers, and detection of clusters with different densities in a given data set. Using a wide variety of data sets, we demonstrate that the proposed cluster seeking algorithm performs at least as well as various other currently popular algorithms and in several cases surpasses them in performance.