  Currently, density-based clustering algorithms are widely applied because
they can detect clusters with arbitrary shapes. However, they perform poorly in
measuring global density, determining reasonable cluster centers or structures,
assigning samples accurately and handling data with large density differences
among clusters. To overcome their drawbacks, this paper proposes a granule
fusion density-based clustering with evidential reasoning (GFDC). Both local
and global densities of samples are measured by a sparse degree metric first.
Then information granules are generated in high-density and low-density
regions, assisting in processing clusters with significant density differences.
Further, three novel granule fusion strategies are utilized to combine granules
into stable cluster structures, helping to detect clusters with arbitrary
shapes. Finally, by an assignment method developed from Dempster-Shafer theory,
unstable samples are assigned. After using GFDC, a reasonable clustering result
and some identified outliers can be obtained. The experimental results on
extensive datasets demonstrate the effectiveness of GFDC.
