基于n球的聚类有效性指标

Ahmed Ben Said, S. Foufou, M. Abidi
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引用次数: 0

摘要

本文提出了一种基于几何形状的聚类有效性指标(CVI)。经典的CVIs是基于分离和紧密度措施的组合,并可能包括集群之间重叠的措施。所提出的CVI结合了紧凑性和重叠使用n球形状的措施。我们在UCI存储库中的几个真实数据集上进行了实验,并将所提出的CVI与广泛使用的CVI的性能进行了比较。结果表明,即使在复杂的数据集上,所提出的CVI算法也比其他方法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cluster validity index based on n-sphere
In this paper, we propose a new cluster validity index (CVI) based on geometrical shape. Classic CVIs are based on a combination of separation and compactness measures and may include a measure of overlap between clusters. The proposed CVI combines measures of compactness and over-lap using n-sphere shape. We conducted experiments on several real data sets from the UCI repository and compared the performances of the proposed CVI with widely used CVIs. Results demonstrate that the proposed CVI performs better than the others even when used with complicated data sets.
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