An internal validity index based on density-involved distance

L Hu, C Zhong - IEEE Access, 2019 - ieeexplore.ieee.org
IEEE Access, 2019ieeexplore.ieee.org
It is crucial to evaluate the quality of clustering results in cluster analysis. Although many
cluster validity indices (CVIs) have been proposed in the literature, they have some
limitations when dealing with non-spherical datasets. One reason is that the measure of
cluster separation does not consider the impact of outliers and neighborhood clusters. In this
paper, a new robust distance measure, one into which density is incorporated, is designed
to solve the problem, and an internal validity index based on this separation measure is then …
It is crucial to evaluate the quality of clustering results in cluster analysis. Although many cluster validity indices (CVIs) have been proposed in the literature, they have some limitations when dealing with non-spherical datasets. One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters. In this paper, a new robust distance measure, one into which density is incorporated, is designed to solve the problem, and an internal validity index based on this separation measure is then proposed. This index can cope with both the spherical and non-spherical structure of clusters. The experimental results indicate that the proposed index outperforms some classical CVIs.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果