In today's data-driven digital era, the amount as well as complexity, such as
multi-view, non-Euclidean, and multi-relational, of the collected data are
growing exponentially or even faster. Clustering, which unsupervisely extracts
valid knowledge from data, is extremely useful in practice. However, existing
methods are independently developed to handle one particular challenge at the
expense of the others. In this work, we propose a simple but effective
framework for complex data clustering (CDC) that can efficiently process
different types of data with linear complexity. We first utilize graph
filtering to fuse geometry structure and attribute information. We then reduce
the complexity with high-quality anchors that are adaptively learned via a
novel similarity-preserving regularizer. We illustrate the cluster-ability of
our proposed method theoretically and experimentally. In particular, we deploy
CDC to graph data of size 111M.

在今天的数据驱动数字时代，收集的数据量和复杂性（如多视图、非欧几里德和多关系）呈指数级增长甚至更快。本文提出了一个简单而有效的复杂数据聚类（CDC）框架，可以以线性复杂度高效处理不同类型的数据。首先，我们利用图过滤来融合几何结构和属性信息。然后，通过一种新颖的保持相似性的正则化器自适应地学习高质量的锚点以降低复杂度。我们从理论和实验上证明了我们提出方法的聚类能力。特别地，我们将 CDC 应用于 111M 规模的图数据中。

CDC：一个用于复杂数据聚类的简单框架

CDC: A Simple Framework for Complex Data Clustering

Recent work on explainable clustering allows describing clusters when the
features are interpretable. However, much modern machine learning focuses on
complex data such as images, text, and graphs where deep learning is used but
the raw features of data are not interpretable. This paper explores a novel
setting for performing clustering on complex data while simultaneously
generating explanations using interpretable tags. We propose deep descriptive
clustering that performs sub-symbolic representation learning on complex data
while generating explanations based on symbolic data. We form good clusters by
maximizing the mutual information between empirical distribution on the inputs
and the induced clustering labels for clustering objectives. We generate
explanations by solving an integer linear programming that generates concise
and orthogonal descriptions for each cluster. Finally, we allow the explanation
to inform better clustering by proposing a novel pairwise loss with
self-generated constraints to maximize the clustering and explanation module's
consistency. Experimental results on public data demonstrate that our model
outperforms competitive baselines in clustering performance while offering
high-quality cluster-level explanations.

本文提出了一种新型的复杂数据聚类算法，利用符号数据进行言语解释，通过最大化模块之间的一致性产生更好的聚类效果和高质量的聚类级别解释，实验结果表明，该模型在聚类性能方面优于竞争对手，并提供高质量的聚类级别解释。