Abstract Most density-based clustering methods have difficulties detecting clusters of hugely different densities in a dataset. A recent density-based clustering CFSFDP appears to have mitigated the issue. However, through formalising the condition under which it fails, we reveal that CFSFDP still has the same issue. To address this issue, we propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters. We then apply Local Contrast to CFSFDP, and create a new clustering method called LC-CFSFDP which is robust in the presence of varying densities. Our empirical evaluation shows that LC-CFSFDP outperforms CFSFDP and three other state-of-the-art variants of CFSFDP.
Only Chen et al. REF have formally identified one necessary condition under which DP fails to detect all clusters of varied densities in a dataset, and proposed a new measure called Local Contrast (LC) to make DP more robust against clusters of varied densities.
Local contrast as an effective means to robust clustering against varying densities
Machine Learning
Machine Learning
Mathematics Computer Science