Evaluating the performance of clustering models is a challenging task where the outcome depends on the definition of what constitutes a cluster. Due to this design, current existing metrics rarely handle multiple clustering models with diverse cluster definitions, nor do they comply with the integration of constraints when available. In this work, we take inspiration from consensus clustering and assume that a set of clustering models is able to uncover hidden structures in the data. We propose to construct a discriminative ordering through ensemble clustering based on the distance between the connectivity of a clustering model and the consensus matrix. We first validate the proposed method with synthetic scenarios, highlighting that the proposed score ranks the models that best match the consensus first. We then show that this simple ranking score significantly outperforms other scoring methods when comparing sets of different clustering algorithms that are not restricted to a fixed number of clusters and is compatible with clustering constraints.

本研究解决了聚类模型性能评估中的挑战，现有指标难以处理多样的聚类定义并缺乏约束整合。我们提出了一种基于距离的集成聚类方法，能够构建区分性排序，证明其在合成场景中优于其他评分方法，显示出对不同聚类算法优异的比较能力并支持聚类约束。

通过集成共识进行区分性排序