提出新指标IQ，衡量聚类差异对质量提升的贡献度。
More Clustering Quality Metrics for ABCDE
- 引入新指标IQ，量化聚类差异与质量改进的关系
- 扩展了ABCDE框架，可评估聚类变化的召回率提升
- 适合需要对比聚类效果的算法优化与评估场景
ABCDE是一种用于评估大规模数据聚类结果的技术。给定基准聚类和实验聚类，ABCDE可通过影响与质量指标表征其差异，从而帮助判断哪个聚类更优。此前已定义GoodSplitRate、BadSplitRate、GoodMergeRate、BadMergeRate及DeltaPrecision等基本质量指标，并基于人工评判进行估计。本文进一步扩展该方法，提出一种刻画聚类变化中召回率差异（DeltaRecall）的技术，并引入新指标IQ，用于衡量聚类差异转化为质量提升的程度。理想情况下，较大的聚类差异应带来显著的质量改善。此外，本文还讨论了如何用ABCDE评估单个聚类的绝对精确率与召回率。
原文摘要 · Abstract (English)
ABCDE is a technique for evaluating clusterings of very large populations of items. Given two clusterings, namely a Baseline clustering and an Experiment clustering, ABCDE can characterize their differences with impact and quality metrics, and thus help to determine which clustering to prefer. We previously described the basic quality metrics of ABCDE, namely the GoodSplitRate, BadSplitRate, GoodMergeRate, BadMergeRate and DeltaPrecision, and how to estimate them on the basis of human judgements. This paper extends that treatment with more quality metrics. It describes a technique that aims to characterize the DeltaRecall of the clustering change. It introduces a new metric, called IQ, to characterize the degree to which the clustering diff translates into an improvement in the quality. Ideally, a large diff would improve the quality by a large amount. Finally, this paper mentions ways to characterize the absolute Precision and Recall of a single clustering with ABCDE.
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