together, delivers robust clustering results in terms of different scaling parameter,
noise level and divergent density distribution across different clusters.
4. We thoroughly evaluate the proposed framework with several closely-related
baseline algorithms on a number of synthetic and benchmark datasets (Section
3.6). The experimental results confirm that the proposed framework, even under
suboptimal parameter settings, outperforms existing approaches for datasets
with noise and heterogeneous density distribution, using different similarity kernels.