The applicability of agglomerative clustering, for inferring both
hierarchical and flat clustering, is limited by its scalability. Existing
scalable hierarchical clustering methods sacrifice quality for speed and often
lead to over-merging of clusters. In this paper, we present a scalable,
agglomerative method for hierarchical clustering that does not sacrifice
quality and scales to billions of data points. We perform a detailed
theoretical analysis, showing that under mild separability conditions our
algorithm can not only recover the optimal flat partition, but also provide a
two-approximation to non-parametric DP-Means objective. This introduces a novel
application of hierarchical clustering as an approximation algorithm for the
non-parametric clustering objective. We additionally relate our algorithm to
the classic hierarchical agglomerative clustering method. We perform extensive
empirical experiments in both hierarchical and flat clustering settings and
show that our proposed approach achieves state-of-the-art results on publicly
available clustering benchmarks. Finally, we demonstrate our method's
scalability by applying it to a dataset of 30 billion queries. Human evaluation
of the discovered clusters show that our method finds better quality of
clusters than the current state-of-the-art.

本文提出了一种可扩展的聚合层次聚类方法，可以在不降低质量的前提下对数十亿个数据点进行聚类，并在公开的聚类基准测试集上实现了最先进的结果，同时还将层次聚类引入作为非参数聚类目标的近似算法。

可扩展的分层凝聚聚类

Scalable Hierarchical Agglomerative Clustering

We present a novel hierarchical distance-dependent Bayesian model for event
coreference resolution. While existing generative models for event coreference
resolution are completely unsupervised, our model allows for the incorporation
of pairwise distances between event mentions -- information that is widely used
in supervised coreference models to guide the generative clustering processing
for better event clustering both within and across documents. We model the
distances between event mentions using a feature-rich learnable distance
function and encode them as Bayesian priors for nonparametric clustering.
Experiments on the ECB+ corpus show that our model outperforms state-of-the-art
methods for both within- and cross-document event coreference resolution.

本文提出了一种新颖的基于层次距离相关性的贝叶斯模型，可以将成对的事件提及之间的距离纳入生成式聚类过程中，进而实现更好的事件聚类和事件消解效果。经过对 ECB + 语料库的实验证明，我们的模型在文档内和跨文档事件消解方面均优于最先进的方法。