基于优化的半监督聚类方法综述

Zahra Ghasemi, H. A. Khorshidi, U. Aickelin
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引用次数: 2

摘要

聚类方法用于将数据点分成不同的组,使每组内的数据点具有较高的相似性。经典的聚类算法是无监督的,这意味着没有任何类型的补充信息可以用来获得更好的聚类结果。然而,在一些聚类问题中,可能有一些补充信息可以用来指导聚类过程。在这些信息存在的情况下,问题是半监督聚类。在一些文章中,半监督聚类问题被建模为一个优化问题。本研究回顾了2013 - 2020年基于优化的半监督聚类论文。这项审查是根据四步程序进行的。它试图探索这些文章中使用的目标函数和优化算法,以及应用领域和监督信息的类型。
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A survey on Optimisation-based Semi-supervised Clustering Methods
Clustering methods are developed for categorizing data points into different groups so that data points within each group have high similarities. Classic clustering algorithms are unsupervised, meaning that there is not any kind of complementary information to be utilized for attaining better clustering results. However, in some clustering problems, one may have supplementary information which can be employed for guiding the clustering process. In the presence of such information, the problem is semi-supervised clustering. In some articles, the problem of semi-supervised clustering is modeled as an optimization problem. In this research, optimization-based semi-supervised clustering papers from 2013 to 2020 are reviewed. This review is conducted based on a four-step procedure. It is attempted to explore objective functions and optimization algorithms used in these articles, as well as application domain and types of supervised information.
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