A novel clustering algorithm based on the gravity-mass-square ratio and density core with a dynamic denoising radius

Abstract

Cluster analysis has been widely used in pattern recognition, image segmentation, document clustering, intrusion detection, market research, and so on. There have been many clustering algorithms for cluster analysis now. However, most of them are unsuitable for complex patterns with large variations in density and manifold structure. Also, they are not robust to noises. To overcome the above deficiencies, we propose a novel clustering algorithm (called DCLGMS) based on the gravity-mass-square ratio (LGMS) and density core (DCore) with a dynamic denoising radius. Our algorithm can obtain the correct clustering result in arbitrarily shaped datasets excluding noises without parameter settings. In this algorithm, DCore can maintain the shapes of clusters, and LGMS can help to extract local core points. In addition, a dynamic denoising radius is beneficial to detect noise points caused by arbitrary reasons and avoid interference at the beginning of the algorithm. The results of experiments on both synthetic datasets and real datasets show that our algorithm has excellent performance.