A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions

聚类分析 深度学习 计算机科学 人工智能 概念聚类 机器学习 水准点(测量) 特征学习 分类 代表(政治) 分类学(生物学) 数据科学 模糊聚类 树冠聚类算法 生物 政治 植物 政治学 法学 地理 大地测量学
作者
Sheng Zhou,Hongjia Xu,Zhuonan Zheng,Jiawei Chen,Zhao Li,Jiajun Bu,Jia Wu,Xin Wang,Wenwu Zhu,Martin Ester
出处
期刊:Cornell University - arXiv 被引量:42
标识
DOI:10.48550/arxiv.2206.07579
摘要

Clustering is a fundamental machine learning task which has been widely studied in the literature. Classic clustering methods follow the assumption that data are represented as features in a vectorized form through various representation learning techniques. As the data become increasingly complicated and complex, the shallow (traditional) clustering methods can no longer handle the high-dimensional data type. With the huge success of deep learning, especially the deep unsupervised learning, many representation learning techniques with deep architectures have been proposed in the past decade. Recently, the concept of Deep Clustering, i.e., jointly optimizing the representation learning and clustering, has been proposed and hence attracted growing attention in the community. Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches. We summarize the essential components of deep clustering and categorize existing methods by the ways they design interactions between deep representation learning and clustering. Moreover, this survey also provides the popular benchmark datasets, evaluation metrics and open-source implementations to clearly illustrate various experimental settings. Last but not least, we discuss the practical applications of deep clustering and suggest challenging topics deserving further investigations as future directions.
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