Relative density-based clustering algorithm for identifying diverse density clusters effectively - Wang, Yuying; Yang, Youlong (Neural computing & applications, 2021)
Clustering is an important part of data mining. The existing clustering algorithm failed in the data set with uneven density distribution. In this paper, we propose a novel clustering algorithm relative density-based clustering algorithm for identifying diverse density clusters effectively called IDDC. It can effectively identify clusters in data sets with different densities and can also handle outliers. We first compute relative density for each data point. Then, the density peak points are screened and the initial clusters are obtained according to these peak points. The strategy for assigning the remaining points is to find unallocated points from the perspective of the cluster, which can effectively identify different density. In experiments, we compare the proposed algorithm IDDC with some existing algorithms on synthetic and real-world data sets. The results show that IDDC performs better than those existing algorithms, especially clustering on data set with uneven density distribution.