Liu Tianmou, Yu Han, Blair Rachael Hageman
Institute for Artificial Intelligence and Data Science State University of New York at Buffalo Buffalo New York USA.
Roswell Park Comprehensive Cancer Center Buffalo New York USA.
Wiley Interdiscip Rev Comput Stat. 2022 Nov-Dec;14(6):e1575. doi: 10.1002/wics.1575. Epub 2022 Jan 9.
Cluster analysis remains one of the most challenging yet fundamental tasks in unsupervised learning. This is due in part to the fact that there are no labels or gold standards by which performance can be measured. Moreover, the wide range of clustering methods available is governed by different objective functions, different parameters, and dissimilarity measures. The purpose of clustering is versatile, often playing critical roles in the early stages of exploratory data analysis and as an endpoint for knowledge and discovery. Thus, understanding the quality of a clustering is of critical importance. The concept of has emerged as a strategy for assessing the performance and reproducibility of data clustering. The key idea is to produce perturbed data sets that are very close to the original, and cluster them. If the clustering is stable, then the clusters from the original data will be preserved in the perturbed data clustering. The nature of the perturbation, and the methods for quantifying similarity between clusterings, are nontrivial, and ultimately what distinguishes many of the stability estimation methods apart. In this review, we provide an overview of the very active research area of cluster stability estimation and discuss some of the open questions and challenges that remain in the field. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification.
聚类分析仍然是无监督学习中最具挑战性但也是最基础的任务之一。部分原因在于,没有可用于衡量性能的标签或金标准。此外,现有的大量聚类方法受不同的目标函数、不同的参数和相异度度量所支配。聚类的目的是多方面的,在探索性数据分析的早期阶段常常发挥关键作用,并且作为知识发现的一个终点。因此,理解聚类的质量至关重要。聚类稳定性的概念已成为评估数据聚类性能和可重复性的一种策略。关键思想是生成与原始数据集非常接近的扰动数据集,并对其进行聚类。如果聚类是稳定的,那么原始数据中的聚类将保留在扰动数据聚类中。扰动的性质以及量化聚类之间相似性的方法并非易事,最终这也是区分许多稳定性估计方法的关键所在。在这篇综述中,我们概述了聚类稳定性估计这个非常活跃的研究领域,并讨论了该领域中仍然存在的一些开放性问题和挑战。本文分类如下:数据科学的统计学习与探索方法>聚类与分类。