Object-based cluster validation with densities
作者：
Highlights：
• In this paper, an object-based clustering validity index with densities referred to as OCVD is proposed. This index uses densities of clusters to capture the exclusive contribution of each data object in both separation and compactness of clusters.
• OCVD is superior to many existing clustering validity indices that capture the properties of clusters by using representative statistics such as mean, variance, diameter, etc. The reason is that those indices might not perform well in capturing the whole characteristics of clusters with arbitrary shapes but OCVD is well capable of doing so.
• Although there are some existing density-based validity indices, studies show that they have problems such as poor performance on clusters with arbitrary shapes which are not necessarily perfectly separated and poor performance due to relying only on some representative data objects in clusters.
摘要
•In this paper, an object-based clustering validity index with densities referred to as OCVD is proposed. This index uses densities of clusters to capture the exclusive contribution of each data object in both separation and compactness of clusters.•OCVD is superior to many existing clustering validity indices that capture the properties of clusters by using representative statistics such as mean, variance, diameter, etc. The reason is that those indices might not perform well in capturing the whole characteristics of clusters with arbitrary shapes but OCVD is well capable of doing so.•Although there are some existing density-based validity indices, studies show that they have problems such as poor performance on clusters with arbitrary shapes which are not necessarily perfectly separated and poor performance due to relying only on some representative data objects in clusters.
论文关键词：Clustering,Clustering validity index,Internal index,Density-based cluster validation,Unsupervised
论文评审过程：Received 7 May 2020, Revised 30 July 2021, Accepted 31 July 2021, Available online 4 August 2021, Version of Record 10 August 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108223
