论文标题
归一化聚类准确性：不对称的外部聚类有效性度量
Normalised clustering accuracy: An asymmetric external cluster validity measure
论文作者
论文摘要
没有，也不会有单一的最佳聚类算法。然而，我们仍然希望能够区分某些任务类型和系统表现不佳的方法。传统上，使用内部或外部有效性度量评估聚类算法。内部度量可以量化所获得的分区的不同方面，例如簇紧密度或点可分离性的平均程度。但是，它们的有效性是值得怀疑的，因为他们认可的聚类有时可能毫无意义。另一方面，外部措施将算法的输出与专家提供的固定地面真相分组进行了比较。在本文中，我们认为通常使用的经典分区相似性分数，例如归一化的互信息，福克斯 - 马洛或调整后的兰德指数，错过了一些理想的特性。特别是，它们无法正确识别最坏情况，也不容易解释。结果，在不同基准数据集上的聚类算法的评估可能很困难。为了解决这些问题，我们提出并分析了一种新的措施：最佳设置匹配精度的一种版本，该版本是对某些相似性关系的单调归一化的，单调的，规模不变的，并且因集群尺寸的不平衡性（但既不对称性也不适合机会）进行纠正。
There is no, nor will there ever be, single best clustering algorithm. Nevertheless, we would still like to be able to distinguish between methods that work well on certain task types and those that systematically underperform. Clustering algorithms are traditionally evaluated using either internal or external validity measures. Internal measures quantify different aspects of the obtained partitions, e.g., the average degree of cluster compactness or point separability. However, their validity is questionable because the clusterings they endorse can sometimes be meaningless. External measures, on the other hand, compare the algorithms' outputs to fixed ground truth groupings provided by experts. In this paper, we argue that the commonly used classical partition similarity scores, such as the normalised mutual information, Fowlkes-Mallows, or adjusted Rand index, miss some desirable properties. In particular, they do not identify worst-case scenarios correctly, nor are they easily interpretable. As a consequence, the evaluation of clustering algorithms on diverse benchmark datasets can be difficult. To remedy these issues, we propose and analyse a new measure: a version of the optimal set-matching accuracy, which is normalised, monotonic with respect to some similarity relation, scale-invariant, and corrected for the imbalancedness of cluster sizes (but neither symmetric nor adjusted for chance).
