论文标题
基于超球的新型集群内部评估指数
A novel cluster internal evaluation index based on hyper-balls
论文作者
论文摘要
在集群分析中评估质量并确定簇数量的最佳数量至关重要。在本文中，进行数据集的多晶状体表征以获得超球。定义了基于高球（HCVI）的集群内部评估指数。此外，提出了一种基于HCVI的最佳簇数的一般方法。所提出的方法可以评估几种经典方法产生的聚类结果，并确定包含具有任意形状的噪声和簇的数据集的最佳群集编号。合成和真实数据集的实验结果表明，新索引的表现优于现有索引。
It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis. In this paper, the multi-granularity characterization of the data set is carried out to obtain the hyper-balls. The cluster internal evaluation index based on hyper-balls(HCVI) is defined. Moreover, a general method for determining the optimal number of clusters based on HCVI is proposed. The proposed methods can evaluate the clustering results produced by the several classic methods and determine the optimal cluster number for data sets containing noises and clusters with arbitrary shapes. The experimental results on synthetic and real data sets indicate that the new index outperforms existing ones.
