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CUBOS An Internal Cluster Validity Index for Categorical Data

机译：CUBOS用于分类数据的内部群集有效性索引

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Internal cluster validity index is a powerful tool for evaluating clustering performance. The study on internal cluster validity indices for categorical data has been a challenging task due to the difficulty in measuring distance between categorical attribute values. While some efforts have been made, they ignore the relationship between different categorical attribute values and the detailed distribution information between data objects. To solve these problems, we propose a novel index called Categorical data cluster Utility Based On Silhouette (CUBOS). Specifically, we first make clear the superiority of the paradigm of Silhouette index in exploring the details of clustering results. Then, we raise the Improved Distance metric for Categorical data (IDC) inspired by Category Distance to measure distance between categorical data exactly. Finally, the paradigm of Silhouette index and IDC are combined to construct the CUBOS, which can overcome the aforementioned shortcomings and produce more accurate evaluation results than other baselines, as shown by the experimental results on several UCI datasets.
机译：内部群集有效性索引是一种用于评估群集性能的强大工具。由于难以测量分类属性值之间的距离，对分类数据的内部群集有效性指数的研究是一个具有挑战性的任务。虽然已经进行了一些努力，但它们忽略了不同分类属性值与数据对象之间的详细分发信息之间的关系。为了解决这些问题，我们提出了一种基于轮廓（CUBOS）的分类数据集群实用程序的新颖索引。具体而言，我们首先清楚地清楚探索聚类结果细节的剪影指数范式的优越性。然后，我们提高由类别距离启发的分类数据（IDC）的改进的距离度量，以测量分类数据之间的距离。最后，剪影索引和IDC的范例组合以构建古铜色，可以克服上述缺点并产生比其他基线的更准确的评估结果，如几个UCI数据集所示。

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