Clustering-boundary-detection algorithm based on center-of-gravity of neighborhood

Abstract

The cluster boundary is a useful model, in order to identify the boundary effectively, according to the uneven distribution of data points int the epsilon neighborhood of boundary objects, this paper proposes a boundary detection algorithm S-BOUND. Firstly, all the points in the epsilon neighborhood of the data objects are projected onto the boundary of the convex hull of the neighborhood, and then calculate the center of gravity of the neighborhood. Finally, detect the boundary object according to the degree of deviation of the center of gravity of the neighborhood with the object. The experimental results show that the S-BOUND algorithm can accurately detect a variety of clustering boundary and remove the noises, the time of performance is also better.

DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3620