提出评估聚类编号方案优劣的新方法，让同一概念的聚类保持稳定编号。
Evaluation of Cluster Id Assignment Schemes with ABCDE
- 将编号差异问题转化为成员变动问题，用ABCDE技术评估。
- 可衡量编号变化的规模与质量，支持大规模数据场景。
- 适合关注聚类稳定性与可追踪性的研究者使用。
聚类编号分配方案为每个聚类分配唯一标识符。其目标是语义编号稳定性，即相同概念的聚类应尽量获得相同编号，以确保跨时间或聚类结果的引用一致性。本文提出评估不同编号分配方案相对优劣的方法：给定历史聚类及编号，以及新聚类的基线和实验编号方案，通过转换为聚类成员关系问题，利用ABCDE技术量化并评估两方案间的编号差异幅度与质量。ABCDE是一种先进且可扩展的聚类成员差异评估方法，适用于包含数十亿项、数百万聚类且部分项重要性不同的真实场景。论文还拓展了基本评估框架，例如可同时评估成员与编号的双重变更。多个实例展示了该方法的实用性。
原文摘要 · Abstract (English)
A cluster id assignment scheme labels each cluster of a clustering with a distinct id. The goal of id assignment is semantic id stability, which means that, whenever possible, a cluster for the same underlying concept as that of a historical cluster should ideally receive the same id as the historical cluster. Semantic id stability allows the users of a clustering to refer to a concept's cluster with an id that is stable across clusterings/time. This paper treats the problem of evaluating the relative merits of id assignment schemes. In particular, it considers a historical clustering with id assignments, and a new clustering with ids assigned by a baseline and an experiment. It produces metrics that characterize both the magnitude and the quality of the id assignment diffs between the baseline and the experiment. That happens by transforming the problem of cluster id assignment into a problem of cluster membership, and evaluating it with ABCDE. ABCDE is a sophisticated and scalable technique for evaluating differences in cluster membership in real-world applications, where billions of items are grouped into millions of clusters, and some items are more important than others. The paper also describes several generalizations to the basic evaluation setup for id assignment schemes. For example, it is fairly straightforward to evaluate changes that simultaneously mutate cluster memberships and cluster ids. The ideas are generously illustrated with examples.
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