GFDC: A granule fusion density-based clustering with evidential reasoning

Abstract

Density-based clustering algorithms are known for their ability to detect irregular clusters, but they have limitations when it comes to dealing with clusters of varying densities. In this paper, we propose a new clustering algorithm called granule fusion density-based clustering with evidential reasoning (GFDC). The approach introduces the concept of sparse degree, which measures both the local density and global density of samples. The sparse degree of samples reflects the stability of samples. Moreover, a core-granule is composed of the neighborhood granule of a sample, of which the sparse degree is minimum in its neighborhood. Then, the core-granules are generated based on the sparse degree and are insensitive to clusters with varying densities. The core samples, which consist of samples in core-granules, are used to form initial clusters through fusion strategies. Additionally, an assignment method is developed from Dempster-Shafer theory to assign border samples and identify outliers. The experimental results demonstrate the effectiveness of GFDC on extensive synthetic and real-world datasets.