Abstract: A new density based clustering method for clustering data points in multidimensional space is described. Each point has a neighborhood consisting of all points that are within a preset cutoff radius or distance. Each point is assigned a density measure based on the number of points in its neighborhood. Any point that has a higher density than any of its neighboring points is the center of a cluster and is assigned a unique cluster ID. Every other point follows a path through the graph of neighboring points such that density is increasing as fast as possible until a cluster center is reached. The algorithm's performance is demonstrated on a one-dimensional, two-dimensional, and 18-dimensional dataset.

Abstract: A new density based clustering method for clustering data points in multidimensional space is described. Each point has a neighborhood consisting of all points that are within a preset cutoff radius or distance. Each point is assigned a density measure based on the number of points in its neighborhood. Any point that has a higher density than any of its neighboring points is the centre of a cluster and is assigned a unique cluster ID. Every other point follows a path through the graph of neighboring points such that density is increasing as fast as possible until a cluster center is reached. The algorithm's performance is demonstrated on a one-dimensional, two-dimensional, and 18-dimensional dataset.