N-sphere Clustering

Abstract: This thesis introduces n-sphere clustering, a new method of cluster analysis, akin to agglomerative hierarchical clustering. It relies on expanding n-spheres around each observation until they intersect. It then clusters observations based on these intersects, the distance between the spheres, and density of observations. Currently, many commonly used clustering methods struggle when clusters have more complex shapes. The aim of n-sphere clustering is to have a method which functions reasonably well, regardless of the shape of the clusters. Accuracy is shown to be low, particularly when clusters overlap, and extremely sensitive to noise. The time complexity of the algorithm is prohibitively large for large datasets, further limiting its potential use.