A Geospatial Implementation of a Novel Delineation Clustering Algorithm Employing the K-means

The overarching objective of this study is to report the implementation and performance of a novel delineation clustering algorithm employing the k-means. This study explores a newly proposed algorithm designed to increase the overall performance of the k-means clustering technique—the Fast, Efficient, and Scalable k-means algorithm (FES-kmeans*). The algorithm reduces the computational load and produce quality clusters. Resulting improvements reside in three major areas: 1) minimization of cluster number fluctuation; 2) efficient handling of large geospatial datasets; and 3) adequate analysis of large geospatial datasets, be it compact or scattered.