Deep Clustering: A Comprehensive Survey
- URL: http://arxiv.org/abs/2210.04142v1
- Date: Sun, 9 Oct 2022 02:31:32 GMT
- Title: Deep Clustering: A Comprehensive Survey
- Authors: Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu,
Philip S. Yu, Lifang He
- Abstract summary: Clustering analysis plays an indispensable role in machine learning and data mining.
Deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks.
Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering.
- Score: 53.387957674512585
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cluster analysis plays an indispensable role in machine learning and data
mining. Learning a good data representation is crucial for clustering
algorithms. Recently, deep clustering, which can learn clustering-friendly
representations using deep neural networks, has been broadly applied in a wide
range of clustering tasks. Existing surveys for deep clustering mainly focus on
the single-view fields and the network architectures, ignoring the complex
application scenarios of clustering. To address this issue, in this paper we
provide a comprehensive survey for deep clustering in views of data sources.
With different data sources and initial conditions, we systematically
distinguish the clustering methods in terms of methodology, prior knowledge,
and architecture. Concretely, deep clustering methods are introduced according
to four categories, i.e., traditional single-view deep clustering,
semi-supervised deep clustering, deep multi-view clustering, and deep transfer
clustering. Finally, we discuss the open challenges and potential future
opportunities in different fields of deep clustering.
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